Department of Epidemiology, German Institute of Human Nutrition, Bergholz-Rehbrücke, Germany. 2

American Journal of Epidemiology Copyright 2003 by the Johns Hopkins Bloomberg School of Public Health All rights reserved Vol. 158, No. 4 Printed in U.S.A. DOI: 10.1093/aje/kwg156 Risk of Hypertension among Women in the EPIC-Potsdam Study: Comparison of Relative Risk Estimates for Exploratory and Hypothesis-oriented Dietary Patterns Matthias B. Schulze 1, Kurt Hoffmann 1, Anja Kroke 2, and Heiner Boeing 1 1 Department of Epidemiology, German Institute of Human Nutrition, Bergholz-Rehbrücke, Germany. 2 Research Institute of Child Nutrition, Dortmund, Germany. Received for publication November 14, 2002; accepted for publication February 20, 2003. Analysis of dietary patterns is considered a useful approach to the examination of diet-disease associations. This study examined the risk of incident hypertension associated with dietary patterns in 8,552 women in the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam Study. The baseline examination was carried out between 1994 and 1998. During 2 4 years of follow-up (through May 15, 2002), 123 incident hypertension cases were verified by medical records. Two exploratory dietary patterns, a traditional cooking pattern (meat, cooked vegetables, sauce, potatoes, and poultry) and a fruits and vegetables pattern (fruits, raw vegetables, and vegetable oil), were identified by exploratory factor analysis and confirmed by confirmatory factor analysis. Additionally, a hypothesis-oriented pattern based on the Dietary Approaches to Stop Hypertension (DASH) Study was defined (fruits, vegetables, and milk products). Patterns associations with disease risk were estimated by Cox regression. While no significant associations were observed for the traditional cooking pattern or the fruits and vegetables pattern after adjustment for potential confounders, women in the third quartile of the DASH pattern were at lower risk than women in the lowest quartile (hazard rate ratio = 0.51, 95% confidence interval: 0.29, 0.89). These results suggest that this hypothesis-oriented pattern might play an important role in the risk of hypertension. diet; factor analysis, statistical; food habits; hypertension; pattern recognition; proportional hazards models Abbreviations: DASH, Dietary Approaches to Stop Hypertension; EPIC, European Prospective Investigation into Cancer and Nutrition; FFQ, food frequency questionnaire. Risk factors for hypertension have long been a focus of epidemiologic research. With regard to diet, however, the dominant approach of examining single nutrients or foods has not been very successful so far given the marginal and inconsistent effects observed, particularly for sodium (1 3), potassium (4, 5), calcium (6 9), and magnesium (10, 11). One explanation as to why clear associations between nutrient intake and hypertension have not been found could be that the single-nutrient approach might not adequately account for the complicated interactions and cumulative effects that seem to be present with regard to hypertension (12). People do not eat isolated nutrients; they eat foods, and they consume them in particular patterns. By trying to separately analyze the effects of single dietary components, one might miss associations between diet and hypertension that are in fact present (13). The possibility that dietary patterns, as measures of overall diet, might have a tremendous effect on blood pressure has been suggested by studies of vegetarian diets (14 17) and by the Dietary Approaches to Stop Hypertension (DASH) Study (18). The DASH Study showed that both a diet high in vegetables and fruits and a diet high in vegetables, fruits, and low-fat milk products significantly reduced blood pressure in normotensive and hypertensive subjects over a period of 6 weeks. However, an earlier review identified no prospective studies that had evaluated effects of dietary patterns on risk of hypertension (19). Although dietary patterns can be predefined for intervention and control diets in trials, they need to be defined on the basis of available dietary data in observational studies. Two general approaches have been used in this context (20). The exploratory approach builds on statistical methods, such as exploratory factor analysis and cluster analysis, to identify Correspondence to Dr. Heiner Boeing, Department of Epidemiology, German Institute of Human Nutrition, Arthur-Scheunert-Allee 114 116, 14558 Bergholz-Rehbrücke, Germany (e-mail: boeing@mail.dife.de). 365

366 Schulze et al. the major dietary patterns of a particular study population (13, 20) independently of their relevance to any disease. In fact, exploratory methods might not be very useful in cases where nutrients or foods relevant in the etiology of a specific disease are not associated with the extracted patterns (21). Furthermore, a major drawback of the currently dominating exploratory methods is the lack of tests of the internal validity of the extracted patterns. On the other hand, the hypothesis-oriented approach focuses on the construction of pattern variables based on available scientific evidence for specific diseases. While this approach might be more valuable in situations where there is evidence of effects of various dietary components, this has not been extensively studied so far. In this study, we compared the effects of dietary patterns from both pattern approaches, exploratory and hypothesisoriented, on the incidence of hypertension in the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam Study and applied confirmatory factor analysis, a method appropriate for testing the validity of exploratory patterns that has hardly ever been used in the context of dietary pattern analysis (22). MATERIALS AND METHODS Study population The study population was selected from participants in the EPIC-Potsdam Study (23), which contributes a general population sample of 27,548 subjects to the EPIC multicenter cohort study (24, 25). The methods of the EPIC- Potsdam Study were approved by the ethical committee of the state of Brandenburg, Germany, and each study participant gave written consent. A population sample of persons meeting the age criteria, men aged 40 64 years and women aged 35 64 years, was provided by the registration offices of the selected municipalities in the Potsdam region. Since the observed number of men with incident hypertension (n = 50) did not allow analysis with an acceptable level of statistical power, the current study was limited to women (n = 16,644). Women reporting a previous diagnosis of hypertension (n = 5,117) or the intake of antihypertensive medication within a 4-week period prior to the baseline examination (n = 406) were excluded. We divided the remaining population of 11,121 women into a learning sample and a study sample to ensure the statistical independence of the exploratory and confirmatory pattern analyses, based on blood pressure measurements undertaken at the baseline examination. A total of 1,994 women with no blood pressure measurements or with elevated blood pressure measurements ( 140 mmhg for systolic pressure or 90 mmhg for diastolic pressure) were assigned to the learning sample, while the remaining 9,127 women with normal blood pressure (<140 mmhg for systolic pressure and <90 mmhg for diastolic pressure) were assigned to the study sample. Further exclusion criteria, applied to both samples, included missing information on dietary intake, estimated basal metabolic rate, physical activity, lifestyle characteristics, and anthropometric measurements; current pregnancy or breastfeeding at baseline; and an outlying total energy intake (less than the first population percentile or greater than the 99th population percentile). After these exclusions, 1,937 women remained for analysis in the learning sample and 8,863 remained in the study sample. Women in the study sample were followed over a period of 2 4 years for incident hypertension, with 97.2 percent successfully responding to questionnaires. Three hundred and fourteen women reported either a new diagnosis of hypertension or use of relevant blood pressure medication during follow-up and were considered to have possible incident hypertension. We verified these possible cases by mailing a questionnaire to the diagnosing or prescribing physician. The verification procedure was complete for 276 possible cases through May 15, 2002. Of those, 123 cases were confirmed as true incident essential hypertension, 125 were not cases of hypertension according to the physician s diagnosis, 24 cases were diagnosed prior to the baseline examination and therefore were prevalent cases, and four cases involved a diagnosis of secondary hypertension. We deleted from the study sample all women with no follow-up, all women with possible hypertension for whom we did not have completed verification, and all women who had prevalent or secondary hypertension; after these exclusions, 8,552 women remained. Dietary, anthropometric, and lifestyle variables The baseline examination consisted of a computer-guided interview and anthropometric measurements. It was carried out between August 1994 and September 1998. In addition, study participants filled out a self-administered food frequency questionnaire (FFQ) and a lifestyle questionnaire at home. The questionnaires were scanned by an optical reader at the beginning of the examination at the study center. The FFQ assessed the usual food and nutrient intakes of participants during the 12 months prior to the examination. Details on the validity and reproducibility of the questionnaire have been previously published (16, 26 29). The FFQ included 148 single food items and questions on specific aspects of diet, such as the fat content of dairy products and the types of fat used for food preparation. Photographs and standard portion sizes were used to support the estimation of portion sizes. Frequency of intake was measured using 10 categories, ranging from never to one time per month or less to five times per day or more. The food items of the FFQ were aggregated into 44 separate food groups (table 1). The grouping scheme was generally based on the principles of the German Food Code (30) and the Eurocode (31); it was also based on food preparation methods (cooked vs. raw vegetables, cooked vs. fried potatoes). The information on portion sizes and frequency of food intake was used to calculate the amount of each food item consumed per day, on average. Smoking status, educational attainment, dietary changes during the previous year, physical activity, and vitamin and/ or mineral supplement use were assessed through personal, computer-guided interviews in the study center. Smoking status was defined as current smoker, ex-smoker, or

Dietary Patterns and Hypertension 367 TABLE 1. Food groupings used in the dietary pattern analysis of the EPIC*-Potsdam Study Food or food group Cooked vegetables Raw vegetables Fresh fruit Cooked potatoes Fried potatoes Meat Poultry Fish Sauce Mushrooms Pasta, rice Pizza Vegetarian dishes Garlic Eggs Soup Dessert Whole-grain bread Other bread Muesli Corn flakes Cake, cookies Confectionery, ice cream Sweet bread spreads Canned fruit Nuts Chips, salt sticks Water Fruit juice Soft drinks Tea Coffee Decaffeinated coffee Beer Spirits Wine Other alcoholic beverages Milk and yogurt Cheese Processed meat Vegetable oils Animal fat Butter Margarine Food items included in group Tomatoes, tomato sauce, sweet peppers, zucchini, eggplant (aubergine), spinach, carrots, asparagus, pea-carrot vegetable mix, leeks, and celery, broccoli, cauliflower, red and white cabbage, and kohlrabi, green peas, green beans, and pea/bean/lentil stew Cucumbers, carrots, sprouts, paprika, tomatoes, onions, and radishes, lettuce, lamb s lettuce, and endive, mix salad, fresh herbs Apples, pears, peaches, cherries, grapes, strawberries, blackberries, raspberries, kiwi fruit, pineapples, mangoes, bananas, oranges, grapefruit, tangerines Salted potatoes, jacket potatoes, mashed potatoes, potato salad, dumplings French fries, potato fritters, fried potatoes Pork, beef, hamburger, minced meat, liver, lamb, roast hare Fried, grilled, or roasted chicken or turkey Fish, canned fish, smoked fish Ketchup, brown and white sauce, salad dressing, sauce for vegetables Fresh mushrooms, mushroom dishes Cooked pasta, cooked rice Pizza, quiche Vegetarian dishes, vegetarian spreads Raw or fried/cooked garlic Boiled eggs, fried eggs, omelettes Vegetable/potato stew, vegetable soup, meat/fish soup, broth, thickened soup Pudding, sweet soufflé Whole-grain bread, dark and whole-grain rolls Rye bread, wheat bread, mixed bread, pale rolls, crisp bread, croissants Whole-grain breakfast cereal, muesli Corn flakes, other refined-grain breakfast cereal Cake, tarts, cookies Chocolate, candy bars, pralines, sugar, ice cream Jam, honey, chocolate spread, peanut butter Canned fruit Nuts Chips, salt sticks, crackers Tap water, mineral water Citrus, apple, orange, grapefruit, grape, cherry, or pineapple juice, multivitamin drinks Soft drinks, malt beer, nonalcoholic beer Black tea, green tea, fruit and herbal teas Regular coffee Decaffeinated coffee, coffee surrogates Beer Spirits Wine, fruit wine, champagne Desert wine, liquor, aperitif Milk, milk drinks, yogurt, sour milk, curd cheese, cream, kefir Fromage, soft cheese, cheese Salami, cold cuts, sausage, ham, fried sausage Vegetable fat used for food preparation (frying, dressing, etc.) Animal fat used for food preparation (except butter) Butter used as a bread spread and for food preparation Margarine used as a bread spread and for food preparation * EPIC, European Prospective Investigation into Cancer and Nutrition.

368 Schulze et al. nonsmoker. Educational attainment was defined as vocational training or a lower degree versus trade school, technical school, or a university degree. Users of vitamin or mineral supplements were defined as persons reporting regular use within the 4 weeks prior to the examination. We determined leisure-time physical activity level as the sum of the energy costs of several activities by multiplying the average daily durations of activities with their metabolic equivalents, based on the method of James and Schofield (32). The frequency, duration, and intensity of activities were assessed with the interview and the lifestyle questionnaire. All anthropometric measurements followed standardized procedures (33) and were performed by trained and quality-monitored staff using instruments under permanent quality control with subjects wearing light underwear. Body height was measured to the nearest millimeter and body weight to the nearest 100 g. Body mass index was calculated as body weight in kilograms divided by the square of body height in meters. Blood pressure was measured in the sitting position on the right arm with the arm elevated at heart level. Eleven oscillometric devices (BOSO-Oszillomat; Bosch and Sohn, Jungingen, Germany) were used. Their reliability was regularly tested on the basis of estimates of inter- and intrainterviewer variances. Furthermore, the validity of the measurements was tested in comparison with the aneroid method, following the protocol of the British Hypertension Society (34). Here, only small systematic differences between both methods were observed. After a resting period of 15 30 minutes, which was usually used for the interviews, three consecutive measurements were performed at intervals of approximately 2 minutes. Cuffs of the size 14 37 cm were used, with larger cuffs (17 41 cm) being available to persons whose arm circumference exceeded 40 cm. Since the combination of the second and third readings has been shown to be the most reliable estimate of a person s blood pressure in the EPIC-Potsdam Study (35), this combination was used in this study to determine blood pressure. Statistical analysis Dietary patterns were defined from exploratory analysis or were hypothesis-oriented. Exploratory patterns were determined in a two-step procedure. First, factor analysis based on the 44 food groups was performed in the learning sample. Two factors were retained on the basis of the eigenvalue >1.0 criterion and a plot of the eigenvalues. Factor loadings greater than or equal to 0.4 were considered significant, as has been suggested for factor analyses (36). Although lower values have been used in other studies (37, 38), such a procedure must be considered critical, since lower loadings limit the interpretability of the retained factor structure (39). The retained structure was then tested in a second step in the study sample with confirmatory factor analysis. The measurement model consisted of the two identified patterns and the corresponding indicator variables. The goodness of fit was determined on the basis of the significance of factor loadings and goodness-of-fit test statistics, particularly the Goodness of Fit Index (40), the Non-normed Fit Index (41), the Comparative Fit Index (42), and the root mean square error of approximation (43). The χ 2 test was not used, since it is very sensitive to large sample sizes. The Goodness of Fit Index, the Non-normed Fit Index, and the Comparative Fit Index measure the explained variance and covariance of the model, with statistics greater than 0.9 being considered acceptable. While the Goodness of Fit Index measures how much better the model fits as compared with no model at all, the Non-normed Fit Index and the Comparative Fit Index compare the model with a null model that has zero correlation between observed variables and no latent factors. Values less than 0.1 for the root mean square error of approximation suggest an acceptable fit of the model. The internal validity of the structure was further tested by calculating Cronbach s coefficient alpha (44), with values greater than 0.7 being considered acceptable as has been suggested (45). The pattern score was calculated as the linear combination of the standardized (to mean 0 and standard deviation 1) indicator variables. This method, while somewhat crude in comparison with the usually applied pattern score determination in factor analysis where each standardized variable is weighted by an equivalent of the factor loadings, has been shown to be robust and to lead to only a minor loss of information (46, 47). Furthermore, Glass et al. (48) reported that the simplified method of score determination in confirmatory factor analysis yields essentially identical results in comparison with a pattern score incorporating weights from the measurement model. The hypothesis-oriented pattern variable was created on the basis of experiences in the DASH Study (18). It represented the sum of three standardized food groups: vegetables (combining the food groups raw vegetables and cooked vegetables ), fruits, and milk products (combining the food groups milk and yogurt and cheese ). The method of score determination was similar to that used for the exploratory patterns, and it resulted in a graded score reflecting essentially a dietary pattern rich in vegetables, fruits, and milk products. It has been shown in a previous study that this pattern score determination is valid for hypothesis-oriented patterns (47). Relative risks of hypertension were estimated with Cox regression, taking the absolute lifetime (birth until the end of the follow-up period or diagnosis) as the time-dependent variable. The pattern variables were categorized on the basis of quartiles, and the relative risk was estimated given the lowest category as the reference group. The models were adjusted for varying confounder information. The significance of linear trends across categories of patterns was tested by assigning each participant the median value for the category and modeling this value as a continuous variable. Furthermore, we tested for the overall heterogeneity of relative risk estimates across categories. RESULTS The factor analysis in the learning sample identified two major factors according to the scree test and the eigenvalue >1.0 criterion. Examination of the factor loading matrix (estimates of the associations between the corresponding observed variables and patterns) for salient items using the cutpoint of 0.4 yielded nine food variables (meat, sauce,

Dietary Patterns and Hypertension 369 TABLE 2. Factor loading matrix from factor analysis of eight food groups in 1,937 women, EPIC*-Potsdam Study, 1994 2002 Food group 1 ( traditional cooking ) * EPIC, European Prospective Investigation into Cancer and Nutrition. poultry, potatoes, cooked vegetables, mushrooms, fruits, raw vegetables, and vegetable oils) that contributed significantly to the two factors. A reanalysis limiting the original variables to these nine food groups yielded essentially the same structure but with mushrooms no longer significantly loading. Reanalysis of the remaining eight food variables yielded the structure shown in table 2. While meat, sauce, poultry, potatoes, and cooked vegetables corresponded to the first factor, labeled traditional cooking, fruits, raw vegetables, and vegetable oils corresponded to the second factor, labeled fruits and vegetables. This structure was tested in the study sample with confirmatory factor analysis. All fit statistics except the Nonnormed Fit Index, which was borderline significant (0.90), confirmed that the proposed structure represented an acceptable fit to the data. Values for the Goodness of Fit Index, the Comparative Fit Index, and the root mean square error of approximation were 0.98, 0.93, and 0.07, respectively. In addition, all standardized factor loadings were statistically significantly different from zero. The internal validity of the structure was further tested by calculating Cronbach s alpha coefficients, which were 0.68 for the traditional cooking pattern and 0.69 for the fruits and vegetables pattern and were therefore very close to 0.7. Table 3 shows the hazard rate ratio estimates for the pattern categories. They were close to 1 and gained no statistical significance for the fruits and vegetables pattern. The hazard rate ratios for the third quartiles of the traditional cooking and DASH patterns were significantly lower than 1 in models adjusted for body mass index and alcohol, but only for the DASH pattern did this association reach statistical significance after multiple confounding variables were controlled. However, as for the fruits and vegetables and traditional cooking patterns, in the multivariate-adjusted model the test for trend (p = 0.20) gained no significance and the hypothesis of homogeneity of hazard rate ratios (p = 0.30) could not be rejected. Factor 2 ( fruits and vegetables ) Meat 0.80 0.05 Sauce 0.71 0.01 Potatoes 0.62 0.08 Poultry 0.59 0.05 Cooked vegetables 0.65 0.20 Raw vegetables 0.06 0.88 Vegetable oil 0.10 0.82 Fruits 0.06 0.67 DISCUSSION Our results suggest that a hypothesis-oriented pattern might have a greater impact on hypertension risk than exploratory patterns have. We observed significant hazard ratios only for the DASH pattern after controlling for potential confounders. However, many of the quartile-specific confidence intervals overlapped across patterns, suggesting that their predictive ability is relatively homogenous. In addition, tests for trend and for heterogeneity across categories of the DASH pattern were not significant. This might be indicative of a lack of robustness of the statistical models due to the relatively small number of cases in the study. Therefore, under- or overestimation of the effects of the patterns cannot entirely be ruled out. It is thus possible that a larger data set or a data set with more person-years of observation might be able to provide a more refined answer to this question. Our results do not support the hypothesis that a link between patterns and disease risk is most likely to be identifiable among those patterns contributing the most to the variance in dietary intake (49). Principal component analysis aims at maximizing the variance of the weighted linear combination of original variables and might therefore be particularly useful for increasing the interindividual variation in the exposure variables. However, this does not necessarily increase the ability to discriminate between diseased and nondiseased persons. Our results suggest that hypothesis-oriented patterns might in fact be more useful in this context. This is supported by a previous study (21), where we showed that exploratory patterns based on factor analysis might explain the intake of single food items and nutrients quite differently. In cases where food items that are likely to be related to the outcome are not well explained, the exploratory factor solution might not be very useful at all for explaining disease risk. The DASH pattern in our study was highly correlated with intake of fruits, vegetables, and milk products and is a pattern that has been shown to have strong effects on blood pressure (18). However, the intake of milk products was not related to both exploratory patterns, and given that milk products probably have an effect on blood pressure because of their high calcium content (7, 9, 50 53), the exploratory patterns were not able to reflect this effect. We note in this context that the application of pattern analysis might not be appropriate in situations where the effect is caused by one specific nutrient or food, since the effect will probably be diluted (54). The dietary pattern approach might be more useful in situations where there is insufficient evidence for effects of single nutrients or foods, as is the case for hypertension, or if there are many established dietary associations. Direct comparisons between hypothesis-oriented and exploratory patterns were reported by Osler et al. (55, 56). Here, an exploratory prudent pattern was found to be more strongly related to all-cause and cardiovascular disease mortality than a hypothesis-oriented healthy food index, which reflected daily intakes of fruits, vegetables, and whole-grain bread. However, no difference with regard to coronary heart disease risk was observed between both patterns. Otherwise, various dietary patterns, both hypoth-

370 Schulze et al. TABLE 3. Cox proportional hazard rate ratios for hypertension according to quartile of dietary pattern (a predefined pattern from the DASH* Study and the exploratory patterns traditional cooking and fruits and vegetables ) in 8,552 women, EPIC*-Potsdam Study, 1994 2002 Dietary pattern and quartile No. of Model 1 Model 2 Model 3 cases HRR*, 95% CI* HRR 95% CI HRR 95% CI DASH pattern Quartile 1 32 1.00 1.00 1.00 Quartile 2 33 0.87 0.54, 1.42 0.84 0.52, 1.37 0.78 0.48, 1.28 Quartile 3 21 0.56 0.32, 0.96 0.54 0.31, 0.93 0.51 0.29, 0.89 Quartile 4 37 0.87 0.54, 1.39 0.81 0.50, 1.31 0.70 0.42, 1.19 p for trend 0.52 0.37 0.20 Traditional cooking pattern Quartile 1 28 1.00 1.00 1.00 Quartile 2 36 0.96 0.58, 1.57 0.93 0.57, 1.53 1.02 0.62, 1.69 Quartile 3 21 0.51 0.29, 0.84 0.48 0.27, 0.85 0.61 0.34, 1.09 Quartile 4 38 0.96 0.59, 1.57 0.88 0.53, 1.44 1.02 0.60, 1.72 p for trend 0.65 0.43 0.86 Fruits and vegetables pattern Quartile 1 31 1.00 1.00 1.00 Quartile 2 28 0.80 0.48, 1.33 0.82 0.49, 1.39 0.82 0.49, 1.37 Quartile 3 31 0.86 0.52, 1.42 0.87 0.53, 1.44 1.01 0.60, 1.68 Quartile 4 33 0.94 0.58, 1.54 0.94 0.57, 1.53 0.94 0.56, 1.57 p for trend 0.81 0.87 0.99 * DASH, Dietary Approaches to Stop Hypertension; EPIC, European Prospective Investigation into Cancer and Nutrition; HRR, hazard rate ratio; CI, confidence interval. Crude. Adjusted for body mass index (weight (kg)/height (m) 2 ) and alcohol consumption (<5, 5 25, or >25 g/day). Adjusted for body mass index, alcohol consumption, smoking status (never smoker, ex-smoker, or current smoker), history of cardiovascular disease or diabetes mellitus (yes/no), regular use of mineral supplements (yes/no), employment status (no work or hourly work vs. part-time or full-time work), education (vocational training or lower degree vs. trade school, technical school, or university degree), systolic blood pressure (mmhg), diastolic blood pressure (mmhg), waist:hip ratio (tertiles), leisure-time physical activity level (tertiles), occupational physical activity (none, light or moderate, or heavy or very heavy), dietary change within the year prior to the baseline examination (yes/no), and total energy intake (MJ/day). Hazard rate ratio from Cox regression with age used as the time variable. esis-oriented and exploratory, were related to disease risk in the Nurses Health Study and the Health Professionals Follow-up Study (38, 57 60). The Healthy Eating Index, which is based on the US Department of Agriculture s Dietary Guidelines for Americans, has been shown to only marginally affect the risk of major chronic diseases (57, 58). These modest effects suggest that exploratory patterns observed within the same study populations (38, 59) might indeed be more important with regard to disease risk. However, a modified Healthy Eating Index based on sound evidence for effects of dietary components has recently been shown to predict chronic disease risk at least as well as exploratory patterns (60). Therefore, whether hypothesisdriven patterns have greater effects on disease risk than exploratory patterns will largely depend on the strength of the evidence on which the hypothesis-driven patterns are based. Several methodological issues must be considered in evaluating our results, however. We ensured in our study that the comparison between hypothesis-oriented patterns and exploratory patterns was not hampered by a lack of internal validity of the latter. We tested the retained factor structure with confirmatory factor analysis and Cronbach s coefficient alpha. Our results suggested that both methods are easily applicable in the context of dietary pattern analysis and that, although Cronbach s alpha coefficients were not quite satisfying, the goodness-of-fit test statistics from the confirmatory factor analysis suggested an acceptable fit of the model. These procedures have hardly ever been applied in other studies of dietary patterns. Gittelsohn et al. (61) reported acceptable levels of Cronbach s coefficient alpha in a crosssectional study on diabetes and obesity status, while Maskarinec et al. (22) successfully validated a pattern structure with confirmatory factor analysis in a cross-sectional study on obesity. Other previous attempts at determining the validity of dietary patterns focused on patterns associations with lifestyle factors such as body size and physical activity (62 67), on patterns associations with biologic markers (68, 69), and on the comparison of retained pattern structures between different dietary assessment instruments (70).

Dietary Patterns and Hypertension 371 Furthermore, there is not much information yet on whether the applied food grouping influences retained exploratory patterns and their subsequent risk estimates (71), giving rise to the chance that risk estimates in our study were in fact under- or overestimated. The applied food grouping has been a matter of debate for factor analysis (39), with no standard being agreed upon. Only a few attempts have been made so far to develop food grouping schemes that are applicable across various populations, such as the Eurocode (31), and the number of analyzed food groups in previous studies that used exploratory factor analysis ranged from 15 (72, 73) to 95 (74). Clearly, further research is needed to define optimal food groups for factor analysis. An additional problem arises from the semiquantitative nature of the applied method for defining the hypothesisoriented DASH pattern. Although the pattern variable in our study had a similar interpretation as the original pattern of the DASH Study, the two studies are not similar in a quantitative sense. Persons with a high score for the DASH pattern had relatively high intakes of fruits, vegetables, and milk products with respect to the study population. On the other hand, quantified diets were applied in the DASH Study, representing specific percentiles of nutrient intake in the US general population (75). Quantitative data from the EPIC- Potsdam Study suggest that the variation in intake was smaller in our study population than in the DASH Study (76). Therefore, the effect of the DASH pattern might have been underestimated. Other methods based on quantitative cutpoints (77) of defining the hypothesis-oriented DASH pattern might have an advantage here. However, FFQs such as the one applied in the EPIC-Potsdam Study have limited usefulness for defining absolute quantitative intake values and are seen to be more useful for ranking individuals with regard to their intake (78). Therefore, a transformation of food variables without defining quantitative cutpoints seems to better account for the semiquantitative character of FFQ data. This was realized with the standardization of food variables in our study. Furthermore, other semiquantitative methods of defining the pattern variables might be applicable. For example, Stampfer et al. (79) and Hu et al. (80) used quintiles for several dietary variables to calculate pattern scores. This approach might be more appropriate to avoid overweighting of extreme intakes that cannot be ruled out by standardizing the food variables. Still, the applied approach of scoring standardized variables is common in factor score determination (36) and has been uniformly applied to both exploratory and hypothesis-oriented patterns in our study to ensure comparability of methods. Although we adjusted for potential confounders, with similar models for exploratory and hypothesis-oriented patterns, confounding of our results cannot be entirely ruled out because of the observational study design. It has previously been suggested that dietary patterns strongly interact with other lifestyle characteristics or rather are part of specific lifestyles (39, 81, 82). While this might strengthen the opinion that the observed patterns may be meaningful (37), it might consequently be impossible to separate pattern effects from the effects of other lifestyle characteristics (39, 81, 82). Our results should not have been biased by disease misclassification. All potential cases were verified through medical records, a procedure that has not been applied in other prospective cohort studies on hypertension. Given the resulting high positive predictive value of the disease classification, the remaining misclassification (nonidentified cases) should not have biased the risk estimates (83). In conclusion, our study suggested that exploratory pattern analysis can be strengthened by testing the internal validity of the extracted factor structure with confirmatory factor analysis and Cronbach s coefficient alpha. Furthermore, the comparison between a hypothesis-oriented DASH pattern, which was based on sound evidence, and exploratory patterns revealed that this hypothesis-oriented pattern might have an important role in the risk of hypertension. ACKNOWLEDGMENTS The recruitment phase of the EPIC-Potsdam Study was mainly supported by the Federal Ministry of Science, Germany (grant 01 EA 9401). 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