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Original Research Communication |
1 From the Departments of Nutrition, Epidemiology, and Biostatistics, Harvard School of Public Health, Boston; the Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston; and the Economic Research Service, US Department of Agriculture, Washington, DC.
See corresponding article on page 1223.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: We tested whether the HEI (scores range from 0 to 100; 100 is best) calculated from food-frequency questionnaires (HEI-f) would predict risk of major chronic disease in women.
Design: A total of 67272 US female nurses who were free of major disease completed detailed questionnaires on diet and chronic disease risk factors in 1984 and repeatedly over 12 y. Major chronic disease was defined as fatal or nonfatal cardiovascular disease (myocardial infarction or stroke, n = 1365), fatal or nonfatal cancer (n = 5216), or other nontraumatic deaths (n = 496), whichever came first. We also examined cardiovascular disease and cancer as separate outcomes.
Results: After adjustment for smoking and other risk factors, the HEI-f score was not associated with risk of overall major chronic disease in women [relative risk (RR) = 0.97; 95% CI: 0.89, 1.06 comparing the highest with the lowest quintile of HEI-f score]. Being in the highest HEI-f quintile was associated with a 14% reduction in cardiovascular disease risk (RR = 0.86; 95% CI: 0.72, 1.03) and was not associated with lower cancer risk (RR = 1.02; 95% CI: 0.93, 1.12).
Conclusion: These data suggest that adherence to the 1995 Dietary Guidelines for Americans, as measured by the HEI-f, will have limited benefit in preventing major chronic disease in women.
Key Words: Diet • nutrition • diet quality • healthy eating index • food guide pyramid • dietary guidelines • cardiovascular disease • cancer • women • food-frequency questionnaire
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The healthy eating index (HEI) is a 10-component indicator of how well Americans' diets conform to the dietary guidelines and the food guide pyramid. The HEI was developed at the US Department of Agriculture (USDA) to track the quality of the American diet over time and to guide nutrition promotion activities (4). In a prospective study of 38622 men followed for 8 y, we found that the HEI score calculated from food-frequency questionnaires (HEI-f) was associated with only a small reduction in overall incidence of major chronic disease [fatal and nonfatal cardiovascular disease (CVD) and cancer and other causes of death combined] (5). This weak inverse association was driven by a moderate inverse association with CVD risk; there was no relation to risk of cancer.
The major causes of death in women differ from those in men throughout much of adult life, with malignant neoplasms ranking highest among adult women aged 45–64 y and CVD ranking highest for men in the same age range (6). After the age of 64 y, however, heart disease is the leading cause of death in both men and women. Therefore, with use of the HEI-f, we examined the overall association between adherence to the dietary guidelines and subsequent risk of major chronic disease in 67272 women from the Nurses' Health Study cohort who were followed for 12 y.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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In 1980 a 61-item food-frequency questionnaire (FFQ) was mailed to participants to obtain information on diet. The FFQ was expanded to 116 items in 1984, primarily by creating individual questions for groups of nutritionally similar foods previously combined in single items. For consistency with our previous study of men, in which the expanded FFQ was used, we considered 1984 as the baseline for the present analysis.
After 
4 mailings, 81757 women returned the 1984 diet questionnaire. We excluded women with previously diagnosed cancer, myocardial infarction (MI), stroke, angina, or other CVD. Women with diabetes were excluded because this disorder is strongly associated with CVD and because it may have caused them to alter their usual diets. We also excluded those with unrealistically low (<2510 kJ/d) or high (>14644 kJ/d) energy intakes and those who left >70 items blank on the FFQ. The final baseline population in 1984 included 67272 women. The study was conducted according to the ethical guidelines of Brigham and Women's Hospital, Boston.
Dietary assessment
Dietary intake data were collected in 1984, 1986, and 1990 with use of similar questionnaires. For each item, a common serving size of food or beverage was specified (eg, 1/2 cup broccoli or 1 cup milk) and participants were asked how often, on average, they consumed this serving size of the item over the previous year. The FFQ provided 9 frequency responses ranging from "never or less than once per month" to "six or more times per day." Separate questions were asked about types of fats and oils used in cooking and addition of salt to food at the table and during cooking. We computed nutrient intake by multiplying the frequency of consumption of each food by the nutrient content of the specified portion, and then summing nutrient contributions from all foods. Nutrient values were derived from USDA sources (7, 8) supplemented with information from food manufacturers and published research.
The development, reproducibility, and validity of the FFQ were documented in detail elsewhere (9–13). In 1986 the expanded FFQ was completed twice at a 1-y interval by a subsample of 191 women from the Nurses' Health Study cohort, many of whom participated in an earlier validation study (9). Two 1-wk diet records were also completed during this interval. The mean correlation coefficient between the energy-adjusted nutrient intakes measured by the second FFQ and the diet records, corrected for week-to-week variation, was 0.63 (L Sampson, E Rimm, G Colditz, et al, unpublished observations, 2000). The mean correlation for nutrients averaged from food records kept by these women during 1980 and 1986 with the 1986 FFQ was 0.68, indicating reasonably valid measures of current and long-term average dietary intake by the FFQ (L Sampson, E Rimm, G Colditz, et al, unpublished observations, 2000).
The healthy eating index
We calculated an HEI score for each FFQ respondent for 1984, 1986, and 1990 by using methods similar to those used in our previous study of men (5), with The Healthy Eating Index Final Report (14) as a guide for these calculations. Listed in Table 1
are the 10 equally weighted components of the HEI, which reflect dietary recommendations based on the food guide pyramid (2) and the Dietary Guidelines for Americans (1). Each component has a minimum score of zero (for nonadherence) and a maximum of 10 (for perfect adherence); intermediate degrees of adherence to the guidelines are calculated proportionately. The scores from the 10 components are added for a total HEI score ranging from 0 (worst) to 100 (best).
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. We assigned food items on the FFQ to their appropriate food groups after applying serving size conversion factors when appropriate to conform to the serving size definitions in the food guide pyramid (16). Consistent with USDA protocol (14), we disaggregated recipes from our database and assigned foods to their appropriate groups. We totaled the average number of daily servings of foods from each food group. We did not adjust the food intakes for total energy because adjustment is not used in the USDA HEI scoring system, which accounts for differences in energy requirements by varying the recommended number of servings by sex and age.
Components 6–9 relate to nutrient intake, which we derived from our database. The total fat, saturated fat, and cholesterol components were scored according to USDA criteria. We calculated the sodium component differently because the 1984 FFQ did not ask about salt added during cooking, which resulted in an underestimate of sodium intake. We therefore divided the participants into 11 equal groups based on the distribution of reported sodium intake and assigned corresponding scores of 0–10 (higher score for less sodium consumed). Although subsequent FFQs did inquire about salt use during cooking, we used this sodium scoring method throughout for consistency.
As in our previous study of men (5), we also quantified the variety component differently. Because the FFQ does not assess foods consumed on a particular day, women were grouped into 11 equal quantiles according to the number of unique foods consumed per month (range: 22–61) and the groups were assigned scores of 0–10. For example, those with the fewest unique foods consumed per month received a score of zero and those with the most received a score of 10. Hereafter, we refer to the estimation of the HEI by the FFQ as the HEI-f. We also calculated HEI scores from food records kept during a validation study of the expanded FFQ in 127 men participating in the Health Professionals Follow-up Study (11, 12). The Pearson correlation coefficient relating the HEI-f to the HEI computed from the diet records, corrected for week-to-week variation in diet records, was 0.72 (95% CI: 0.59, 0.81). This indicates that the HEI can be estimated reasonably well from the FFQ.
For comparison, the USDA calculated HEI scores for a demographically similar subset of women from the nationwide Continuing Survey of Food Intake by Individuals (CSFII; n = 428), using one 24-h recall and 2 food records. The women in the CSFII subset were employed, had 
12 y education, and were of the same age range as the Nurses' Health Study participants.
Outcome ascertainment
The primary endpoint for this study was combined major chronic disease, defined as CVD, cancer, or death, whichever came first. We also examined CVD and cancer as separate outcomes.
CVD was defined as fatal or nonfatal MI or fatal or nonfatal stroke. We asked all women who reported incident MI or stroke on any biennial follow-up questionnaire to confirm the report and provide permission to review medical records. Study physicians blinded to risk factor status reviewed the records. MI was confirmed by using the World Health Organization criteria (17). Strokes were confirmed if characterized by a typical neurologic defect of sudden or rapid onset, lasting 24 h, and attributable to a cerebrovascular event (18).
When a new cancer case was identified, we asked the participant (or next of kin) for permission to obtain hospital records and pathology reports. We included all confirmed cancers except nonmelanoma skin cancer and in situ breast cancer because of the relatively low mortality from these lesions.
The major chronic disease endpoint also included all nontraumatic deaths. Deaths were reported by next of kin, coworkers, or postal authorities, or ascertained by a search for nonrespondents in the National Death Index (19). If not listed in the National Death Index, nonrespondents were assumed to be alive. We attempted to confirm the cause of each death, including fatal MI, stroke, and cancer, by using medical records or autopsy reports.
Statistical analyses
Each participant contributed follow-up time from the return of her baseline questionnaire in 1984 until the diagnosis of CVD or cancer, until death, or until June 1, 1996, whichever came first. During follow-up, confirmed cases were censored from subsequent follow-up. Therefore, the cohort at risk included only those free of disease at the beginning of each 2-y follow-up interval. Overall follow-up based on potential person-years was >95% complete.
HEI-f quintiles were defined by using a cumulative-diet-average scoring method (20). This method incorporated repeated measures of diet to capture long-term intake and to reduce measurement error due to intraindividual variation in diet over time (20). With this approach, the 1984 HEI-f score was used to predict outcome in 1984–1986 and an average of the 1984 and 1986 HEI-f scores was related to outcome between 1986 and 1990. The average of the 1984, 1986, and 1990 scores was used to predict outcome from 1990–1996. If an FFQ was not completed in 1986 or 1990, the previous HEI-f score was carried forward. We did not update diet for women who reported a diagnosis of angina, hypercholesterolemia, diabetes, or hypertension during follow-up because changes in diet after development of these intermediate endpoints may have confounded the exposure-disease association. We also examined the relation between baseline diet in 1984 and risk of all outcomes.
We calculated relative risks (RRs) as the incidence rate of disease among women in each quintile of HEI-f score divided by the incident rate for women in the lowest quintile of HEI-f score. We adjusted RRs for age (5-y categories) by using the Mantel-Haenszel method (21). The Mantel extension test (22) was used to test for linear trend. To simultaneously adjust for 2 covariates, we used pooled logistic regression with 2-y time increments to compute odds ratios as an estimate of RR (23). This approach is regularly used in prospective cohort analyses with repeated measures of exposure and has been shown to closely approximate Cox regression analysis with time-dependent covariates (24). We tested for significant linear trends with use of multivariate logistic models by modeling median quintile values as a continuous regression variable. In addition to analysis by quintiles, we examined each outcome according to USDA-defined HEI score categories of poor (<51), needs improvement (51–80), and good (>80).
To examine potential interactions, we stratified by age (60 and >60 y at baseline), smoking status (never, past, and current), current multivitamin use (yes or no), menopausal status (pre- or postmenopausal), family history of heart disease (yes or no), and family history of cancer (yes or no). Models with and without interaction terms were compared by using likelihood ratio tests to formally test for interactions (25).
To examine the influence of individual HEI-f components on the incidence of major chronic disease, CVD, and cancer, each of the 10 components was entered into the multivariate model as a continuous (0–10) variable. Components were entered into the models both individually and simultaneously. We also used stepwise regression to determine which HEI-f components were most strongly and independently associated with the outcomes of interest, using a P value of 0.10 as the criteria for inclusion.
We considered several covariates for inclusion in the final multivariate models. Those that remained were established risk factors and variables that changed the ß coefficient for the HEI-f-outcome association by >10%. The following covariates were updated biennially: age, exogenous hormone use and menopausal status, cigarette smoking, body mass index (BMI; in kg/m2), and multivitamin and vitamin E supplement use. Leisure time physical activity was assessed in 1986, 1988, 1992, and 1994 by inquiring about average weekly time spent performing specified activities during the past year. Activity was modeled in metabolic equivalents (METs) per week, with 7 METs equal to 
1 h of jogging/wk. We applied 1986 reports of activity to the 1984–1986 time period and carried 1988 values forward to the 1990–1992 time period. Total energy intake and alcohol consumption were assessed in 1984, 1986, and 1990. Energy intake was updated by using the same method as for the HEI-f. A baseline diagnosis of hypercholesterolemia or hypertension was included as a covariate only in the major chronic disease and CVD multivariate models. The same baseline exclusions were used for all outcomes. We present RRs and 95% CIs. All P values are two-sided.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The mean HEI-f score at baseline (1984) was 64.4 ± 12.5 and ranged from 11.7 to 99.3 (10th to 90th percentile: 47.5–80.2). Study participants had higher mean scores for vegetables, fruit, and meat and lower mean scores for grains than did the CSFII subset (16); other scores were similar (Table 1). Because the HEI-f scores for the variety and sodium components were based on their distribution in the present cohort, the mean score was 5.0 by definition. The distribution of total HEI-f scores in the cohort overlapped considerably with the HEI scores reported for the CSFII subset (Figure 1).
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. Higher HEI-f scores were associated with other healthful lifestyle behaviors: women with high scores were less likely to smoke, more likely to exercise, and more likely to take multivitamin supplements and supplemental vitamin E. In addition, energy consumption was higher (in part reflecting physical activity) and BMI slightly lower with higher HEI-f quintiles. As expected, servings of milk, fruit, vegetables, and grains and the variety of foods consumed increased across HEI-f quintiles and total fat and saturated fat decreased across HEI-f quintiles. The number of meat group servings and dietary cholesterol changed only slightly across quintiles.
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. Food sources of several antioxidants, minerals, and fiber all increased across HEI-f quintiles. Likewise, glycemic load, an indicator of blood glucose response induced by total carbohydrate intake (26), was positively associated with HEI-f score. Percentage of energy from monounsaturated and polyunsaturated fat decreased slightly across quintiles.
Lifestyle and diet characteristics of the CSFII subset are presented in Table 3 for comparison. In this group of women, median HEI quintile scores were similar to those in our study. Patterns of smoking behavior, age, BMI, energy intake, and other dietary variables were also qualitatively similar.
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. With adjustment for only age, the risk of overall major chronic disease was 19% lower for women in the highest HEI-f quintile than for those in the lowest quintile (RR = 0.81; 95% CI: 0.75, 0.87). Control for smoking changed the RR in the highest HEI-f quintile to 0.92 (Table 4). In our final multivariate model, the association between HEI-f and major chronic disease was null. Results were similar whether or not BMI was included in the model (without BMI, RR = 0.96; 95% CI: 0.89, 1.05). Although the HEI-f score was strongly inversely associated with CVD risk with adjustment for only age (a 41% reduction in risk), this relation was markedly attenuated when smoking was added to the model (RR = 0.81; 95% CI: 0.69, 0.96). Control for additional covariates (primarily exercise) further weakened the estimate (RR = 0.86; 95% CI: 0.72, 1.03). Although a weak inverse association for HEI-f and total cancer risk was noted in the age-adjusted models, models controlled for smoking, activity, and other confounders showed no association. Results using baseline HEI-f scores (rather than cumulatively updated values) were similar for all outcomes. We did not observe any significant interactions by smoking status, age, menopausal status, multivitamin use, or family history of cancer or CVD.
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). Compared with a good score as the reference, those with poor scores were not at higher risk of overall major chronic disease or cancer. However, those with poor scores had an increased risk of CVD (RR = 1.27; 95% CI: 1.01, 1.61). Women in the needs improvement category, which constituted 74% of the cohort [and 71% of the CSFII subset (15)], were not at increased risk.
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. A high cholesterol score (representing lower cholesterol intake) was consistently inversely associated with major chronic disease risk. The sodium score (representing lower sodium intake) was directly associated with outcome when entered individually in the model, but was no longer significant in the model controlled for all other components. The total fat score (representing less total fat) was directly related to major chronic disease in the model controlled for all other components. When saturated and total fat were in the same model, the total fat term represented other fats (mainly polyunsaturated and monounsaturated fat); a low intake of these fats predicted a higher risk of disease. After stepwise regression, the cholesterol score (inversely) and sodium score (directly) remained in the model for major chronic disease.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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These findings are generally comparable with those from our study of men of similar age (5). In both cohorts, the association between HEI-f and CVD risk was moderately inverse, but there was no association with cancer risk. Because CVD and cancer affect men and women at different rates throughout much of adult life, the relative proportions of CVD and cancer incidence in the populations studied will influence the relation between HEI-f and overall chronic disease. In this study of women, cancer cases exceeded CVD cases 4-fold, whereas in the male Health Professionals Follow-up Study (5), cancer cases exceeded CVD cases 1.5-fold. This difference in the relative occurrence of disease is likely to account for the small differences in associations of the HEI-f with major chronic disease in the 2 cohorts (RR = 0.89 in men and 0.97 in women, for those with the highest compared with the lowest scores).
Others reported inverse associations between indexes of recommended dietary patterns and risk of total (27–30), cardiovascular (29–31), and cancer (29–31) mortality. Huijbregts et al (29) found that an 8-component index of adherence to the World Health Organization guidelines for prevention of chronic disease was inversely associated with all-cause, cardiovascular, and cancer mortality over a 20-y period. Likewise, a Mediterranean diet index was inversely associated with all-cause mortality in Greece (27) and in Denmark (32). Kant et al (30) recently found that a recommended food score (RFS) was associated with a 30% lower risk of total mortality in a cohort of US women. The results were widely interpreted as evidence of the efficacy of the current Dietary Guidelines for Americans. However, the RFS, which ranged from 0 to 23, was heavily weighted by fruit and vegetables, which contributed 15 points, and included no meat or refined grain products and ignored the fat and carbohydrate recommendations emphasized by the food guide pyramid. In contrast, the diet quality index (a more comprehensive measure of US dietary guidance than the RFS) was not associated with total mortality in a large cohort of men and women after important confounders were controlled for (J Seymour et al, unpublished observations, 2000)
A direct comparison of these studies with our findings is not feasible. Most used relatively crude component scores (binary or 0–2 points) and control for confounders varied. In addition, these studies used mortality as an endpoint. Because case fatality can be influenced by early diagnosis, access to optimal medical care, and compliance with treatment, the use of mortality as an endpoint is potentially confounded by behaviors that are difficult to measure and control.
Several possible explanations may account for the lack of an overall association between the HEI-f and risk of chronic disease. The HEI was originally developed for use with 24-h recalls and food records in the CSFII survey population (4). Because FFQs are designed to measure long-term dietary intake and contain a defined list of foods, our approximation of the HEI varied slightly from the original method. As noted, we defined variety according to distribution in the present cohort. Likewise, the sodium score was defined according to distribution of intake in our study population because of underestimation of sodium in 1984. Results considering 1986 as baseline (and using original HEI sodium scoring cutoffs) were not materially different. A second possibility is that the method we used to measure dietary intake did not measure actual food consumption well. However, the FFQ we used has been validated with use of both food records and biological markers of dietary intake (9, 11–13, 33). Moreover, the intake of several nutrients and foods increased or decreased in the expected direction across HEI-f quintiles, indicating that the index captured recommendations of the dietary guidelines, and there was a wide distribution in total HEI-f scores. In the age-adjusted models, we found strong and significant associations, especially for CVD. However, estimates were markedly attenuated after confounders, especially smoking and exercise, were controlled for. Furthermore, our validation of the HEI-f scoring method with use of data from the Health Professionals Follow-up Study cohort indicated that the total HEI score was estimated reasonably well by the FFQ.
Another strong possibility for the lack of overall association in this study is that the HEI and the dietary guidelines do not describe an optimal diet. Improvement in the HEI is likely to strengthen its association with major chronic disease, especially CVD risk. For example, the 1995 dietary guidelines emphasize reductions in total and saturated fat, cholesterol, and sodium and an increase in complex carbohydrates—recommendations influenced mainly by expected reductions in CVD risk. Some of these factors were independently predictive of disease in our analysis of individual HEI-f components. However, additional multiple and complex mechanisms are involved in the pathogenesis of CVD (34) and attention to other important dietary factors may improve the association with CVD risk. For example, other types of fat (35–40), antioxidants (34, 41), folate and vitamin B-6 (42, 43), and carbohydrate quality (26, 44) have all been associated with CVD development. In this and in our study in men (5), the total fat component score (reflecting a low-fat diet) was directly associated with disease when saturated fat was controlled for. In this context, the total fat score can be interpreted as a measure of other types of fat (eg, polyunsaturated and monounsaturated fats). Thus, these findings suggest that limiting unsaturated fats, which is usually done by increasing carbohydrate if energy intake is maintained, is detrimental. This is consistent with metabolic studies indicating that replacing unsaturated fats with carbohydrate increases triacylglycerol and decreases HDL cholesterol (40). Furthermore, low-fat, high-carbohydrate diets provide a higher glycemic load, aggravate hyperinsulinemia (45), and may thus increase the risk of diabetes and coronary artery disease (26, 39, 46).
The role of diet in the development of cancer is less well understood and is more complex than that for CVD (47). The dietary guidelines may be too nonspecific; for example, potatoes are not related to cancer risk reduction (48, 49) but contribute importantly to vegetable consumption as defined by the food guide pyramid and the HEI. Furthermore, in prospective studies (50), low-fat diets consistently have little or no relation to breast cancer risk—the predominant cancer in this cohort. The food guide pyramid also combines foods that have been directly associated with chronic disease risk with foods inversely associated with disease (eg, fish and red meat, as well as whole grains and highly refined starches) (44, 51). Because there are no upper limits on food servings in the HEI, high consumption of red meat and refined carbohydrates can yield perfect component scores (52).
This population comprised mostly white women with some college education, though the socioeconomic level of the nurses overlaps considerably with that of women in the general US population. This homogeneity increases the internal validity of the study by reducing confounding by factors that are hard to measure. However, the ability of the HEI-f score to predict lower chronic disease risk in women of other racial and educational backgrounds should also be evaluated.
In summary, we observed no association between the HEI-f score and risk of overall major chronic disease in women. Our results may reflect a combined effect of the weak associations between dietary factors in midlife and risk of breast cancer (the major disease in our outcome measures) and the shortcomings of the HEI in defining an optimal diet for prevention of CVD and other conditions. Continued refinement of the HEI, and the dietary guidelines, may improve the ability of the HEI to predict major chronic disease in women and its usefulness as a tool for describing an optimal diet. Dietary guidelines should continue to be evaluated for their efficacy in reducing the incidence of diseases of major public health concern.
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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-linolenic acid and risk of fatal ischemic heart disease among women. Am J Clin Nutr 1999;69:890–7.This article has been cited by other articles:
|
|
 |
|
  S. K Gao, S. A. Beresford, L. L Frank, P. J Schreiner, G. L Burke, and A. L Fitzpatrick Modifications to the Healthy Eating Index and its ability to predict obesity: the Multi-Ethnic Study of Atherosclerosis Am. J. Clinical Nutrition, July 1, 2008; 88(1): 64 - 69. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Reedy, P. N. Mitrou, S. M. Krebs-Smith, E. Wirfalt, A. Flood, V. Kipnis, M. Leitzmann, T. Mouw, A. Hollenbeck, A. Schatzkin, et al. Index-based Dietary Patterns and Risk of Colorectal Cancer: The NIH-AARP Diet and Health Study Am. J. Epidemiol., July 1, 2008; 168(1): 38 - 48. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Estaquio, K. Castetbon, E. Kesse-Guyot, S. Bertrais, V. Deschamps, L. Dauchet, S. Peneau, P. Galan, and S. Hercberg The French National Nutrition and Health Program Score Is Associated with Nutritional Status and Risk of Major Chronic Diseases J. Nutr., May 1, 2008; 138(5): 946 - 953. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. A. McNaughton, K. Ball, D. Crawford, and G. D. Mishra An Index of Diet and Eating Patterns Is a Valid Measure of Diet Quality in an Australian Population J. Nutr., January 1, 2008; 138(1): 86 - 93. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Boynton, M. L Neuhouser, M. H Wener, B. Wood, B. Sorensen, Z. Chen-Levy, E. A Kirk, Y. Yasui, K. LaCroix, A. McTiernan, et al. Associations between healthy eating patterns and immune function or inflammation in overweight or obese postmenopausal women Am. J. Clinical Nutrition, November 1, 2007; 86(5): 1445 - 1455. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. B. Dixon, A. F. Subar, U. Peters, J. L. Weissfeld, R. S. Bresalier, A. Risch, A. Schatzkin, and R. B. Hayes Adherence to the USDA Food Guide, DASH Eating Plan, and Mediterranean Dietary Pattern Reduces Risk of Colorectal Adenoma J. Nutr., November 1, 2007; 137(11): 2443 - 2450. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. J Fogli-Cawley, J. T Dwyer, E. Saltzman, M. L McCullough, L. M Troy, J. B Meigs, and P. F Jacques The 2005 Dietary Guidelines for Americans and risk of the metabolic syndrome Am. J. Clinical Nutrition, October 1, 2007; 86(4): 1193 - 1201. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. J McCabe-Sellers, S. Bowman, J. E Stuff, C. M Champagne, P. M Simpson, and M. L Bogle Assessment of the diet quality of US adults in the Lower Mississippi Delta Am. J. Clinical Nutrition, September 1, 2007; 86(3): 697 - 706. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Drogan, K. Hoffmann, M. Schulz, M. M. Bergmann, H. Boeing, and C. Weikert A Food Pattern Predicting Prospective Weight Change Is Associated with Risk of Fatal but Not with Nonfatal Cardiovascular Disease J. Nutr., August 1, 2007; 137(8): 1961 - 1967. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Taubert, R. Roesen, C. Lehmann, N. Jung, and E. Schomig Effects of Low Habitual Cocoa Intake on Blood Pressure and Bioactive Nitric Oxide: A Randomized Controlled Trial JAMA, July 4, 2007; 298(1): 49 - 60. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. J. Fogli-Cawley, J. T. Dwyer, E. Saltzman, M. L. McCullough, L. M. Troy, J. B. Meigs, and P. F. Jacques The 2005 Dietary Guidelines for Americans and Insulin Resistance in the Framingham Offspring Cohort Diabetes Care, April 1, 2007; 30(4): 817 - 822. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. J. Fogli-Cawley, J. T. Dwyer, E. Saltzman, M. L. McCullough, L. M. Troy, and P. F. Jacques The 2005 Dietary Guidelines for Americans Adherence Index: Development and Application J. Nutr., November 1, 2006; 136(11): 2908 - 2915. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. E Millen, M. J Pencina, R. W Kimokoti, L. Zhu, J. B Meigs, J. M Ordovas, and R. B D'Agostino Nutritional risk and the metabolic syndrome in women: opportunities for preventive intervention from the Framingham Nutrition Study. Am. J. Clinical Nutrition, August 1, 2006; 84(2): 434 - 441. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X. Gao, P. E. Wilde, A. H. Lichtenstein, and K. L. Tucker The 2005 USDA Food Guide Pyramid Is Associated with More Adequate Nutrient Intakes within Energy Constraints than the 1992 Pyramid J. Nutr., May 1, 2006; 136(5): 1341 - 1346. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. A. Breslow, P. M. Guenther, and B. A. Smothers Alcohol Drinking Patterns and Diet Quality: The 1999-2000 National Health and Nutrition Examination Survey Am. J. Epidemiol., February 15, 2006; 163(4): 359 - 366. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. T. Fung, F. B. Hu, M. L. McCullough, P. K. Newby, W. C. Willett, and M. D. Holmes Diet Quality Is Associated with the Risk of Estrogen Receptor-Negative Breast Cancer in Postmenopausal Women J. Nutr., February 1, 2006; 136(2): 466 - 472. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. T Fung, M. L McCullough, P. Newby, J. E Manson, J. B Meigs, N. Rifai, W. C Willett, and F. B Hu Diet-quality scores and plasma concentrations of markers of inflammation |
