The relation of dietary patterns to future survival, health, and cardiovascular events in older adults

Journal of Clinical Epidemiology 56 (2003) 1224–1235

The relation of dietary patterns to future survival, health, and cardiovascular events in older adults Paula Diehra,b,c,*, Shirley A.A. Beresfordc,d a Departments of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, USA Department of Health Services, University of Washington, Box 357660, Seattle, WA 98195, USA c Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, P.O. Box 19024, Seattle, WA 98109, USA d Department of Epidemiology, University of Washington, Box 357236, Seattle, WA 98195, USA Accepted 24 April 2003 b

Abstract Background: There have been few long-term follow-up studies of older adults who follow different dietary patterns. Methods: We cluster-analyzed data on dietary fat, fiber, protein, carbohydrate, and calorie consumption from the U.S. Cardiovascular Health Study (mean age ⫽ 73), and examined the relationship of the dietary clusters to outcomes 10 years later. Results: The five clusters were named “Healthy diet” (relatively high in fiber and carbohydrate and low in fat), “Unhealthy diet” (relatively high in protein and fat, relatively low in carbohydrates and fiber); “High Calorie,” “Low Calorie,” and “Low 4,” which was distinguished by higher alcohol consumption. The clusters were strongly associated with demographic factors, health behaviors, and baseline health status. The Healthy diet cluster had the most years of life and years of healthy life, and the Unhealthy diet cluster had the fewest. The Low 4 cluster had the best cardiovascular outcomes. Differences were not usually large. Conclusions: Older adults who followed the healthy eating pattern had somewhat longer and healthier lives, and the cluster with more alcohol consumption was associated with fewer cardiovascular events. The unhealthy eating pattern had the worst outcomes. 쑖 2003 Elsevier Inc. All rights reserved. Keywords: Nutrition; Diet; Cluster analysis; High protein; Low carbohydrate; Atkins diet; Self-rated health

1. Introduction/Background Many people describe themselves as following a special diet. Some follow variants of the Dietary Guidelines for Americans [1,2], which recommends a variety of grains, fruits, and vegetables and is low in saturated fat and moderate in total fat. Others try to follow a high protein, or a low calorie, or a low carbohydrate diet, or some combination of these factors, for weight loss or weight maintenance. There is relatively little literature on the association of nutrition patterns with outcomes for older adults. Much of the existing literature reports relationships of a single nutrient to outcomes, even though it is likely that the overall makeup of the diet is more important [3,4]. Some investigators have addressed this concern by creating “clusters” of persons who have similar combinations of nutrients [5–14]. These studies have examined cross-sectional associations of the clusters with other dietary factors and with some demographic and health measures. None of them has examined how long-

* Corresponding author. Tel.: 206-543-8004; fax: 206-543-3286. E-mail address: [email protected] (P. Diehr). 0895-4356/03/$ – see front matter 쑖 2003 Elsevier Inc. All rights reserved. doi: 10.1016/S0895-4356(03)00202-6

term outcomes are related to cluster membership. The goal of our study is to examine, in older adults, the association of dietary patterns (clusters) with cross-sectional demographic and health factors, and with long-term outcomes including survival, years of healthy life, and incident heart disease and stroke. We hypothesized that older adults who followed the healthy eating pattern would have better health behaviors, more years of life and years of healthy life, and better cardiovascular outcomes than those who followed other patterns. As dietary choices may have been made because of health problems, it is necessary to control carefully for baseline health status.

2. Methods 2.1. Study design: the cardiovascular health study The Cardiovascular Health Study (CHS) is a populationbased longitudinal study of 5,888 adults aged 65 and older at baseline (mean age 73) [15]. Subjects were recruited from a random sample of the Medicare eligibility lists in four U.S. counties. Persons who did not expect to remain in the area

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for the next 3 years, or who were institutionalized, using a wheelchair at home, or receiving treatment for cancer at baseline were ineligible. Extensive baseline data were measured for all subjects, involving a home interview and clinic examination. After baseline, subjects had an annual clinic visit, and a brief telephone interview 6 months after each scheduled visit. Two cohorts were followed, the first with 10 years of follow-up (mean baseline age 73, 57% female, 4.7% African-American, n ⫽ 5,201) and a second with 7 years of follow-up (mean age 73, 63% female, all African-American, n ⫽ 687). Data collection began in 1989, and follow-up is virtually complete for all surviving subjects [16]. The nutrition data that are the subject of this article were collected in about 1990 for the first cohort. A second wave of nutrition data, collected in 1996, was used only for validation.

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a special diet [19,20]. In the current analysis we clustered the participants based on their consumption in 1990 of calories, fat, fiber, protein, and carbohydrates, chosen because these nutrients are the major components of popular diets. For 16 additional nutrients, listed in Table 2 and the appendix, we examined the association of the nutrient with the clusters. 2.3. Demographic factors and health behaviors Self-reported sociodemographic factors and health behaviors, used as covariates, included age, gender, race, years of education, marital status, smoking status, and being on a medically prescribed diet. Exercise intensity was based on the highest intensity exercise over the 2 weeks prior to the CHS baseline examination [21]. We refer to low intensity or no exercise as a sedentary lifestyle.

2.2. Nutrition data 2.4. Baseline descriptors of health Dietary intake in the 12 months before baseline was estimated by asking each participant to sort a deck of 99 cards, each with a picture of a food or food group, into five categories, according to how often they ate the pictured food: almost every day, one to four times per week, one to three times per month, 5–10 times per year, or never [17]. The resulting food frequencies were then converted to nutrient intake using information from the NCI nutrient data base. To reduce respondent burden, portion size was not ascertained. Chapters 5 and 6 of Willett’s textbook on nutritional epidemiology discuss characteristics of food frequency questionnaires, which are summarized here [18]. Although such questionnaires are commonly used in epidemiologic research, they have limitations. They are usually designed to distinguish among persons with different levels of intake, rather than to provide valid estimates of total intake. Estimates of total energy intake are likely biased for a variety of reasons, but research has shown that information about nutrients after adjustment for energy intake is reasonably accurate. Research has also determined that adding information about portion size to information about food frequencies provides little improvement in the ability to distinguish among persons with different levels of nutrient intake. Finally, Willett notes that epidemiologic studies typically find no relationship between energy intake and body weight, due to some combination of under-reporting by the obese and differences in metabolism and physical activity. This background information suggests that the available data should provide reasonable information about the nutrients after adjustment for energy intake, but not about unadjusted levels of the nutrients. In the tables we present some absolute nutrition data for reference, with means shown to 2 decimal places only to ensure enough significant digits for the micronutrients in Table 2. The limitations of the absolute data should be kept in mind. Earlier cross-sectional analyses demonstrated significant associations in the expected directions between macronutrient intake and sex, age, race, body mass index, and use of

Self-reported baseline health covariates included history of arthritis, cancer, or diabetes; fair or poor self-rated health status; having any difficulties in performing (instrumental) activities of daily living; and unintended weight loss of 10 pounds or more in the year before baseline. Clinical covariates included body mass index (BMI—measured weight over height squared), hypertension, cardiovascular disease (prevalent heart disease, peripheral vascular disease, or cerebrovascular disease), the Modified Mini Mental State Examination (MMSE), and depressive symptoms (CESD score) [22]. These measures are described in more detail elsewhere [23–26]. 2.5. Outcomes Morbidity and mortality outcomes were identified through patient or family member self-report, review of hospital and physician records, and death certificates. Events were then adjudicated by a physician review panel [16]. Events included myocardial infarction (MI), Angina, Stroke, and congestive heart failure in the 10 years following baseline. We also measured years of healthy life and years of life. “Years of healthy life” (YHL) is the number of years (of a total of 10) in which the person reported being in excellent, very good, or good health (as opposed to fair, poor, or dead) [27]. For example, a person who reported being in excellent, very good, or good health in 3.5 of the study years and in worse health (or dead) in the other 6.5 years would have 3.5 years of healthy life of a possible 10. “Years of life” (YOL) is the number of years a person was alive in the 10 years after baseline. Mean YOL is the area under the usual survival curve, and is used here for comparability with YHL. A formal “survival analysis” is not needed because all persons were followed for 10 years. We also estimated the remaining YOL and YHL after the end of the study (that is, from 2000 on), as a function of each person’s age, sex, and health status in 2000 [28].

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2.6. Relative nutrient values Absolute nutrient values tend to be positively correlated because a person who eats a lot will tend to have high levels of nutrients, and a person who eats little will have low levels. To remove this association with quantity, we regressed each variable on the number of calories the person consumed, and used the residual from the regression as the relative nutrient value [29]. The regression equations for fat, fiber, protein, and carbohydrate were as follows: protein ⫽ ⫺3.97 ⫹ 0.046*calories (R2 ⫽ 0.86); fiber ⫽ 5.98 ⫹ 0.00676*calories (R2 ⫽ 0.41); fat ⫽ ⫺10.19 ⫹ 0.0437* calories (R2 ⫽ 0.83); and carbohydrate ⫽ 29.19 ⫹ 0.100* calories (R2 ⫽ 0.79). We performed similar regressions for the other nutrients listed in Table 2. The R2 values ranged from 0.31 to 0.86. To estimate calorie consumption from a person’s age, sex, and height, we used linear regression with backwards elimination to predict calorie consumption from various transformations of age, sex, and height and their interactions. The resulting equation was: calories ⫽ 24,065 ⫹ 765.4/(height in meters)2 ⫹ 237.4*(sex: one for male, zero for female) ⫹ 95.3*age⫺15,901* log10(age); (R2 ⫽ 0.026). Relative consumption was defined as the difference between the person’s actual intake and her predicted intake. For instance, a person who ate 2,000 calories would be predicted to have ⫺3.97 ⫹ 0.046*(2,000) ⫽ 88 g of protein; if she had actually consumed 100 g, her relative consumption would be 100⫺88 ⫽ 12 g. A 73-year-old man 183 cm tall was predicted to consume 24,065 ⫹ 765.4/(1.832) ⫹ 237.4* (1) ⫹ 95.3*73⫺15,901*1.863 ⫽ 1,866 calories. He, in fact, consumed 1,809, resulting in a relative calorie intake of 1,809⫺1,866 ⫽ ⫺57, or 57 calories fewer than the average man of the same age and height. These relative values are used throughout the article. As expected, the correlations among the five primary dietary variables (fat, fiber, protein, carbohydrate, calories) were strongly positive on the absolute scale. On the relative scale, however, carbohydrates and fiber were positively correlated, and were negatively correlated with consumption of fat and protein. For the cluster analysis we also standardized the five primary nutrients as “z-scores.” 2.7. Clusters We clustered subjects on the five nutrition variables that are components of many popular diets for health or weight loss: relative fat, fiber, protein, carbohydrate, and calories. Other nutrition variables were available; however, they were highly correlated with the five we chose, and inclusion of multiple highly correlated measures often causes the factor they have in common to create its own cluster, even if it is not an important factor. We report the relationship of the other nutrition variables to the clusters. We used the k-means clustering procedure in SPSS to define subgroups of persons with similar dietary patterns. After “k” is specified by the user, the procedure chooses k persons who are “far

apart” on the five dietary measures as the preliminary basis of the k clusters. Next, each person in the database is assigned to the cluster whose means on the five variables are closest to that person’s values, and the means for that cluster are recomputed. The assignment process is repeated until persons no longer change clusters. Mnemonic names were then given to the clusters, based on the patterns of their cluster means. We examined solutions with two through six clusters. We report detailed results for only the five-cluster solution, chosen in part because one of the clusters somewhat resembles the high-protein low-carbohydrate diets that are currently in vogue in the United States, such as the Atkins diet [30]. The cluster solutions for other values of k are described briefly. 2.8. Analysis After creating the five clusters, we used analysis of variance to test whether the means of the nutrition, demographic, and health behavior variables differed significantly among clusters. Because this was almost always the case, these factors were controlled in most of the analyses. To determine whether the baseline health or outcomes for any cluster were significantly different from those in Cluster 5 (which we later call the Healthy diet cluster), we used multiple regression, regressing each health variable on dummy variables denoting cluster membership. We controlled first for demographics (age, sex, race, education, and marital status) and health behaviors (smoking, sedentary lifestyle, and being on a medically prescribed diet). In a second regression for the outcomes, we also controlled for the 13 baseline health variables, out of concern that persons may have chosen their diets because of their health. Regressions were run with and without the baseline health measures, because some of them may have been in the causal pathway. For example, diet may affect the onset and severity of diabetes, suggesting that diabetes should not be controlled in a regression of outcome on diet. The reader may choose the level of control that seems most appropriate. Consistency of the results for various levels of adjustment would suggest that the results were robust. Subjects of this analysis had complete nutrition data, but a small number had missing data for a few of the other variables. All available data were used in those cases. The regression with the most missing data excluded only 8% of the cases. We also performed supplementary analyses that are reported in less detail comparing Cluster 4 to all others, Cluster 2 to Cluster 3, Cluster 1 to Cluster 2, and Cluster 4 to Cluster 5. We did not control these analyses for the other nutrition variables, because they were highly correlated with the variables that were used in the cluster analysis. Including them would remove the effects of the clusters, which we wanted to study. Our goal is to study associations with the clusters, rather than to specify which specific nutrients are responsible for the associations.

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We also looked briefly at the second wave of nutrition data. We cluster-analyzed the data to see if the same clusters emerged. Because the clustering algorithm does not provide a classification rule, we used linear discriminant analysis to approximately classify persons into clusters based on the 1990 data, and used that discriminant function to classify the 1990 and 1996 data. We then investigated whether persons tended to be in the same cluster at both times, and presented additional information for the larger sample of African-Americans available in 1996.

3. Findings The cluster analysis of 4,610 persons with nutrition data determined five clusters of sizes 751, 766, 1,350, 922, and 821. Fig. 1 presents schematic information about these clusters. It shows the mean consumption of relative nutrients, as z-scores. For example, in Cluster 1, the relative fat and protein consumption were about one standard deviation above the mean, and the carbohydrate and fiber consumption were about one standard deviation below the mean. As a mnemonic device, we labeled the clusters according to their most prominent features. The pattern in Cluster 5 is high in fiber and carbohydrate, but low in fat. We refer to it as the Healthy diet cluster, because it is similar to the Dietary Guidelines for Americans [1] and the American Dietary Association (ADA) recommendations [2]. (Whether or not the Healthy diet cluster is actually associated with healthy outcomes is the subject of this article.) Cluster 1 is almost the opposite of Cluster 5, and so is labeled the Unhealthy diet cluster. Cluster 2 (High Calorie) is high in calories but as expected on other nutrients, and Cluster 3 (Low Calorie) is low in calories. Cluster 4 is above expected on fat, and below expected on the other 4 nutrients. We refer to this cluster as “Low 4.”

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We also examined solutions with 2 through 6 clusters (not shown). The two-cluster solution comprised an unhealthy and a healthy diet cluster; the three-cluster solution added a low calorie cluster; with four, a cluster with some of the features of the Low 4 cluster emerged; with five, a high calorie cluster appeared; and with six a high protein cluster was added. Table 1 presents the mean value, by cluster, of the five nutrients used to form the clusters. Both the absolute and relative values are presented. For example, there were 4,610 persons in the study, who averaged 69.78 g of fat per day. In Cluster 1, the mean was 92.53, which was 13.13 g of fat more than that of the average person who consumed the same number of calories (relative fat ⫽ 13.13). Mean calorie consumption ranged from 1,359 to 2,719 kilocalories per day. There is considerable difference between the absolute and relative levels of nutrients. For example, Cluster 2 consumed more absolute fat than Cluster 1, but because Cluster 2 also consumed more calories, Cluster 1 has higher relative fat than Cluster 2. Note also that although Cluster 1 had low relative consumption of carbohydrate, the absolute level (191 g) would not be thought of as low in any sense. Thus, care must be taken in the characterization of these clusters. Analysis of variance determined that the cluster means differed significantly for each variable (row). This is not surprising, because these variables were used to create the clusters. Table 2 presents information on the 16 nutrients that were not used in the cluster analysis. The first column shows the mean overall absolute values (column labeled “all”). The mean relative values by cluster are in columns 1–5. For example, mean retinol intake was 1,153 mg overall. In Cluster 1, the mean relative retinol was 22. Persons in Cluster 5, the Healthy diet cluster, had higher than expected (relative to their calorie intake) consumption of all vitamins and minerals (indicated by positive relative values in the table) and below expected consumption of fats and alcohol (negative values). Cluster 3 had a similar pattern to Cluster 5,

Fig. 1. Mean relative nutrients by cluster (z-scores).

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Table 1 Mean absolute and relative nutrients per day by cluster-variables used in cluster analysisa All Number of persons Percent of persons Absolute nutrients Fat (g) Dietary fiber (g) Carbohydrate (g) Protein (g) Kilocalories Relative nutrients Fat (g) Dietary fiber (g) Carbohydrates (g) Protein (g) Kilocalories a

1: Unhealthy

4610 100

2: Hi Cal

3: Low Cal

4: Low 4

5: Healthy

751 16.3

766 16.6

1350 29.3

922 20.0

821 17.8

69.78 18.26 210.84 80.47 1813.11

92.53 15.42 190.86 101.4 2024.56

109.67 24.50 304.51 121.28 2719.00

46.39 16.17 173.11 61.63 1358.63

64.87 11.73 159.06 56.89 1503.30

55.73 25.80 261.94 80.71 1869.75

.00 .00 .00 .00 .00

13.13 ⫺4.21 ⫺40.71 11.16 200.43

⫺.87 .30 5.10 ⫺1.89 748.12

⫺2.98 .98 7.56 2.53 ⫺431.92

8.52 ⫺4.45 ⫺21.88 ⫺9.37 ⫺304.80

⫺15.83 6.96 44.97 ⫺2.00 83.82

All variables differ significantly by cluster.

and Cluster 4 had the opposite pattern. Clusters 1 and 2 were mixed. Cluster 4 was above the expected in alcohol consumption, the cluster’s most distinctive feature. The cluster means were significantly different for every row, even though these nutrients were not used to define the clusters. This demonstrates the strong associations between the nutrients in Table 1 and Table 2. Table 3 presents sociodemographic factors and health behaviors by cluster. The cluster means differed significantly on every variable but age, % White among women, and marital status for men and women separately. Clusters 3 and 5 generally had demographics and health behaviors associated with better health, and Clusters 1 and 4 had the least favorable.

Table 4 shows 13 health-related variables collected at baseline, by cluster. For example, the average BMI was 26.41, ranging from 26.05 in Cluster 5 to 27.29 in Cluster 1 (a BMI of 25–30 is considered overweight) [31]. Note that the Low-Calorie and High-Calorie clusters had about the same BMI, as is often the case with food frequency data [20]. Higher values in Table 4 indicate worse health, except for MMSE. Cluster 1, the Unhealthy diet cluster, had the worst values for most of these variables. Clusters 4 and 5 often had the best values. The symbols in Table 4 show whether a cluster was significantly better (⫹) or worse (⫺) than Cluster 5, adjusted for the variables in Table 3. About a quarter of the comparisons (12 of 52) were statistically significant, with Cluster 1 always showing worse health than

Table 2 Mean relativea nutrients per day by clusterb variables not used in cluster analysis

Vitamins Retinol (mg) Vit A (IU) Thiamine (mg) Riboflavin (mg) Niacin (mg) Vit C (mg) Minerals Calcium (mg) Iron (mg) Potassium (mg) Phosphorus (mg) Sodium (mg) Fats and alcohol Saturated fat (gm) Cholesterol (mg) Oleic acid (gm) Linoleic acid (gm) Alcohol (kcal) a b

All (Absolute)

1: Unhealthy

2: Hi Cal

1152.56 13,866.77 1.46 2.17 21.19 199.57

21.55 ⫺1446.20 ⫺.16 ⫺.09 .44 ⫺55.07

12.06 82.83 .01 .01 ⫺.19 4.90

859.13 15.10 3276.63 1338.49 3228.58

⫺63.78 ⫺.65 ⫺270.61 22.98 36.66

24.87 331.98 24.92 13.15 293.95

5.45 104.61 5.46 1.29 ⫺33.35

3: Low Cal

4: Low 4

5: Healthy

30.34 741.06 .06 .11 .91 9.40

⫺113.98 ⫺3233.33 ⫺.19 ⫺.29 ⫺2.84 ⫺48.91

46.59 3681.68 .26 .22 1.48 85.41

1.78 .06 ⫺6.51 ⫺17.22 ⫺15.18

39.47 .53 133.21 54.16 48.46

⫺103.49 ⫺2.18 ⫺497.68 ⫺154.92 ⫺202.62

109.83 2.11 598.93 82.69 129.65

⫺.39 ⫺8.76 ⫺.29 .08 ⫺8.01

⫺1.07 ⫺11.36 ⫺.99 ⫺.70 ⫺37.30

3.45 23.51 3.35 2.25 122.28

⫺6.75 ⫺95.07 ⫺6.86 ⫺2.63 ⫺38.39

The first column shows the absolute (not relative) mean, and the other columns show the relative means (mean deviation from the expected value). All variables differ significantly by cluster.

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Table 3 Demographic factors and health behaviors by clustera (mean or percent)

Age (mean) % Men % White % ⬎ High school education % Married % Current smoker % Ever smoked % Sedentary lifestyle % Medically prescribed diet a

All

1: Unhealthy

2: Hi cal

3: Low cal

4: Low 4

5: Healthy

72.84 42.86 94.77 21.06 69.04 11.48 53.68 56.45 13.35

72.50 48.20 92.01 16.06 73.24 16.78 58.32 62.48 11.19

73.02 43.21 94.26 20.97 70.46 10.57 53.52 56.92 10.72

72.83 37.93 95.56 23.57 65.43 8.52 50.78 55.26 15.81

72.93 55.10 95.55 17.97 72.13 16.29 61.67 61.13 8.04

72.90 32.03 95.62 25.00 66.34 6.95 45.37 47.20 19.73

The cluster means differed significantly on every variable but age, % White among women, and marital status for men and women separately.

Cluster 5, but Clusters 2–4 showing a mix of positive and negative differences. We next looked at longitudinal outcomes, adjusted first for the variables in Table 3, and then also adjusted for all of the variables in Table 4. Table 5 shows that in the 10 years after baseline, participants averaged 6.4 years of healthy life and 8.5 years of life (about 35% died in this period). When we controlled only for the variables in Table 3, Clusters 1 and 2 had significantly lower YOL and YHL than Cluster 5, and Cluster 4 had significantly lower YHL than Cluster 5. After control for the variables in both Tables 3 and 4, Cluster 1 remained significantly lower than Cluster 5 on YOL and YHL, and Cluster 2 was significantly lower for YOL. We also estimated the remaining YOL and YHL after 2000 for each person [28], and added this to the observed YOL and YHL, yielding the predicted values until time of death, shown in Table 5. For example, persons in Cluster 1 averaged 5.95 YHL in the 10 years of follow-up, and would be expected to have a total of 7.86 years if followed to death. Mean lifetime YOL was about 4 years higher than 10-year YOL, and the increase in YHL was about 2 years. Results were similar to those for 10-year YOL and YHL, but with three fewer significant differences.

In the 10 years following baseline, 24% had one or more episodes of angina, 21% had congestive heart failure, and 13% had a stroke or MI. Interestingly, Cluster 4 had significantly less angina and MI than Cluster 5, controlling for either set of variables. In a post hoc analysis comparing Cluster 4 to the others (not shown), it was significantly better than Clusters 3 and 5 on both angina and MI, with or without control for baseline health variables. It was also significantly better than Cluster 1 for angina until the baseline health variables were controlled. We also compared Cluster 2 to Cluster 3 on the variables in Tables 2–5 (not shown). Despite their similarities on the clustering variables, Cluster 2 had significantly different (and better) values on all the nutrients in Table 2 but retinol, cholesterol, and vitamin C. In Table 3, Cluster 2 members were significantly more likely to be male or married, and less likely to be on a medically imposed diet. Among the Table 4 variables, after adjustment for the Table 3 variables, Cluster 2 had significantly more depression and a lower MMSE than Cluster 3. There were significant differences for all variables in Table 5, with Cluster 2 having lower YHL and YOL but also less angina and MI than Cluster 3. We also compared Cluster 1 to Cluster 2, to examine their apparent similarities (not shown). In Table 2, there were

Table 4 Baseline health measures by clustera,b(mean or percent)

BMI Wt loss, prev. year (%) Depressive symptoms Cholesterol (mg/dL) Hypertension (%) Diabetes (%) Heart disease (%) Cancer (%) Arthritis (%) Fair/poor health (%) ADL (%) IADL (%) MMSEb

All

1: Unhealthy

2: Hi cal

3: Low cal

4: Low 4

5: Healthy

26.41 7.16 4.50 211.43 42.25 11.00 25.49 15.11 51.38 23.45 7.23 25.41 90.64

27.29⫺ 8.52 4.74 208.16 42.27 15.05⫺ 27.03 15.35 51.62 26.63⫺ 9.32⫺ 25.70 89.94

26.23 7.31 4.80⫺ 210.66 42.30 9.66 24.02 15.54 49.60⫹ 22.85 8.65 26.08 89.96⫺

26.48 7.41 4.35 212.00 44.36⫺ 12.07⫺ 26.22 13.21⫹ 51.47 23.11 7.27 26.19 91.18

26.08 5.64 4.33 210.31 41.54 9.44 23.43⫹ 15.76 48.58 24.95⫺ 4.99 24.08 90.61

26.05 7.06 4.44 215.48 39.51 8.53 26.55 16.85 55.83 19.98 6.46 24.73 91.01

a Comparing clusters 1–4 to Cluster 5, controlled for the variables in Table 3, results were: ⫺ Significantly worse than Cluster 5; ⫹ Significantly better than Cluster 5. b Lower values are better except for MMSE.

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Table 5 Outcome variables by cluster (mean or %)

YHL, 10 years YOL, 10 years YHL, Lifetime YOL, Lifetime % Angina event % MI event % Stroke event % CHF event

All

1: Unhealthya

2: Hi cala

3: Low cala

4: Low 4a

5: Healthy

6.41 8.48 8.48 12.76 23.75 13.21 12.99 20.54

5.95⫺⫺ 8.23⫺⫺ 7.86⫺⫺ 12.14⫺⫺ 25.30 14.51 14.38 24.37

6.23⫺ 8.30⫺⫺ 8.24⫺ 12.40 23.11 11.62 13.84 19.84

6.20 8.61 8.79 13.16 24.30 14.15 12.07 19.26

6.20⫺ 8.32 8.21 12.38 21.26⫹⫹ 11.71⫹⫹ 13.23 19.96

6.92 8.85 9.08 13.46 24.85 13.64 12.18 20.46

Significant differences (P ⬍ .05, two-tailed test). Significantly worse than Cluster 5 after control for Table 3 variables and after control for Table 3 and Table 4 variables. ⫺ Significantly worse than Cluster 5 after control for Table 3 variables. ⫹⫹ Significantly better than Cluster 5 after control for Table 3 variables and after control for Table 3 and Table 4 variables. a

⫺⫺

significant differences for every variable but alcohol, retinol, and sodium, but the direction of the differences varied. In Table 3, there were significant differences only for smoking and education, with Cluster 2 more favorable. In Table 4 controlled for Table 3, Cluster 2 had significantly lower BMI and less diabetes. In Table 5 controlled for Tables 3 and 4, there were no significant differences in outcome. Finally, we compared Cluster 3 to Cluster 5 on Tables 2 and 3 (not shown). For Table 2, Cluster 5 had significantly more favorable levels of nutrients for every variable but retinol and alcohol. For Table 3, Cluster 5 had significantly fewer men, smokers, sedentary lifestyle, and more on a physician-recommended diet. Tables 1–5 showed only unadjusted cluster means. Space considerations prevented us from providing more detail for all variables, but Table 6 presents the mean difference between Cluster 5 and the other four clusters, at various levels

of adjustment. The largest standard error of the four mean differences is also shown. Stroke and congestive heart failure were omitted because there were never significant intercluster differences. In Table 5, mean 10-year YHL in Cluster 1 was 5.95 years compared to 6.92 in Cluster 5, a difference of 0.97 years of healthy life (DIF 1). That is the value in the first row of Table 6, which is labeled “None,” for no adjustment. Values for the other cluster differences are also shown on that line, as is the maximum standard error, which could be used to compute approximate confidence intervals for these differences. The second line of Table 6 shows that after adjustment for demographics and health behaviors (the Table 3 variables), DIF 1 was reduced to 0.64 years, and after additional adjustment for the baseline health variables (Table 3⫹4 variables) that difference dropped to 0.34 years (about 4 months). These differences remained significant at the 0.05

Table 6 Mean differences from Cluster 5 adjusted for Table 3 and Table 3⫹4 variablesa Var

Adjusted:b

Dif 1c

Dif 2

Dif 3

Dif 4

Max SEd

YHL 10

None Table 3 Table 3⫹4 None Table 3 Table 3⫹4 None Table 3 Table 3⫹4 None Table 3 Table 3⫹4 None Table 3 Table 3⫹4 None Table 3 Table 3⫹4

⫺.97** ⫺.64** ⫺.34** ⫺.62** ⫺.39** ⫺.27** ⫺1.22** ⫺.81** ⫺.51** ⫺1.32** ⫺.82** ⫺.58** .45 ⫺.62 ⫺.80 .87 ⫺.53 ⫺.45

⫺.69** ⫺.49** ⫺.26* ⫺.55** ⫺.38** ⫺.24** ⫺.83** ⫺.54** ⫺.31* ⫺1.06** ⫺.65** ⫺.41* ⫺1.74 ⫺2.15 ⫺1.69 ⫺2.02 ⫺2.66 ⫺2.22

⫺.33** ⫺.19 ⫺.12 ⫺.24** ⫺.11 ⫺.07 ⫺.29 ⫺.15 ⫺.05 ⫺.30 ⫺.10 ⫺.06 ⫺.55 ⫺.65 ⫺.69 .51 .05 ⫺.19

⫺.72** ⫺.35** ⫺.25* ⫺.53** ⫺.20* ⫺.22* ⫺.87** ⫺.27 ⫺.18 ⫺1.09** ⫺.26 ⫺.24 ⫺3.59* ⫺5.16** ⫺5.22** ⫺1.93 ⫺3.89** ⫺3.58**

.17 .16 .13 .13 .12 .12 .24 .20 .18 .31 .24 .24 2.15 2.17 2.19 1.71 1.73 1.77

YOL 10

YHL Life

YOL Life

Angina

MI

Compared to Cluster 5: *means P ⬍ .05 1-tailed; **means P ⬍ .05, two-tailed. “None” means unadjusted; Table 3 means adjusted for demographics and health behaviors; Table 3⫹4 means adjusted for demographics, health behaviors, and baseline health variables. c Dif 1 is the mean difference between Cluster 1 and Cluster 5; Dif 2 ⫽ Cluster2-Cluster 5, etc. d Largest standard error for the four difference estimates. a

b

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level with a two-tailed test (denoted by two asterisks). Tenyear YOL, adjusted for the Table 3 variables, was 0.20 years lower in Cluster 4 than in Cluster 5 (DIF 4), and was significantly different from zero if a one-tailed test was performed, but not for a two-tailed test (denoted by a single asterisk). Only 4 of the 72 tabled differences were positive, showing that the strong overall trend is for Cluster 5 to have the best YOL and YHL but the worst angina and MI outcomes. The differences in YOL and YHL generally became smaller as other variables were adjusted for, showing that much of the apparent difference between the clusters could be accounted for by non-nutrition variables. However, adjustment for the other variables increased the advantage of Cluster 4 for angina and MI. Cluster 3 was close to Cluster 5 on all of the measures (mean differences were near zero), and Cluster 1 behaved similarly to Cluster 2. 3.1. Comparison of clusters with two standard diets Anderson et al. used sample menus to estimate the nutritional intake for a person following several standard diets while consuming 1,600 calories a day [32]. One was the diet recommended by the ADA [2], which is similar to that in the Nutritional Guidelines for Americans [1], and is low in saturated fat and high in complex carbohydrates and dietary fiber. Using the Anderson data we calculated the expected nutrients for each person in the study if he had been following the ADA diet, but at his own calorie level. (We assumed that a person eating, e.g., 3,200 calories would be expected to consume twice as much fat, fiber, carbohydrate, and protein as a person consuming 1,600 calories.) We then calculated the ratio of the mean observed nutrients to the expected nutrients under the ADA diet. For example, a person on a 1,600 calorie ADA diet would be expected to consume 35.2 g of fat [32]. Cluster 5, which averaged 1,870 calories (Table 1) would be expected to consume 1,870/ 1,600*35.2 ⫽ 41.1 g. They actually averaged 55.7, or 1.33 times as much as recommended. As expected, the Healthy diet cluster was close to the ADA diet (ratios close to 1). Persons in the other clusters averaged more fat and less fiber, carbohydrate, and protein than the ADA diet. It is therefore reasonable to consider Cluster 5 as a representative of a “healthy” diet. The Atkins diet, intermittently popular in the United States, is recommended by some for weight loss or weight maintenance [30]. It strongly recommends eating high fat foods and avoiding vegetables, fruits, breads, cereals, starchy vegetables, and most dairy products; it allows two small green salads daily. The Unhealthy diet cluster is superficially similar to the Atkins diet. However, Cluster 1 averaged 7 times as much carbohydrate, three times as much fiber, 70% as much fat, and 56% as much protein as expected under the 14-day “induction” form of the Atkins diet. Thus, Cluster 1 is not as extreme as the induction phase of the Atkins diet. After 14 days, dieters are allowed to increase their carbohydrate intake as long as they keep losing weight,

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which might make them more like Cluster 1. We had hoped to provide some long-term results for the Atkins-type diet, but did not have enough people with extreme diets to do so. 3.2. Second wave of nutrition data A second wave of nutrition data were collected in 1996. There were differences in the 1996 instruments, some persons had died by 1996, and the cohort of African-Americans was available at that time. The 1990 data were available for 4,610 persons, of whom 218 (4.6%) were Black. The 1996 data were available for 3,789 persons, of whom 594 (15.7%) were Black. We used these data to address several questions. 3.2.1. Were similar clusters found in 1996? The Unhealthy, Low-Calorie, and Healthy diet clusters emerged from a five-cluster analysis of the 1996 data. This suggests that researchers with similar data would find similar clusters to ours. 3.2.2. Persistence of cluster membership Cluster analysis assigns persons to clusters, but does not provide a rule for assigning a new person to a cluster. We used linear discriminant analysis to predict cluster membership in 1990 from the 1990 nutrients, their squares, and their interactions. The classification equations, which classified 92% of the 1990 data correctly, were then applied to the 1996 data. Of the 2,949 persons who had nutrition data at both times, about 40% were in the same cluster in 1990 and 1996, and an additional 20% changed to the Low Calorie cluster. Persistence was highest for the Low-Calorie cluster (49%), and lowest for the Unhealthy diet cluster (26%). True persistence may be higher, because the data collection method changed over time. 3.2.3. Black/White differences Race was significantly associated with cluster membership at both times. In 1990, Blacks were more likely than Whites to be in the Unhealthy diet cluster, but in 1996, based on the larger sample of Blacks, they were more likely than Whites to be in the High-Calorie cluster and less likely to be in the Unhealthy diet cluster. Blacks were thus consistently in the less healthy diet clusters (1 and 2). These differences are consistent with cultural differences in food choices that are integral to different racial and ethnic groups [33].

4. Summary We identified five common dietary patterns in older adults, based on their relative consumption of fat, fiber, protein, carbohydrate, and calories. Cluster membership was associated with many variables that were not used in creating the clusters. For example, the Healthy diet cluster was associated with better nutrition on other variables, as well as with favorable demographics and better health behaviors. The

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Low 4 cluster included most of the heavier drinkers, even though alcohol consumption was not one of the cluster variables. Cross-sectional analysis cannot distinguish between “you are what you eat” and “you eat what you are.” It is important to remember that the many factors that were associated with cluster membership could help to explain the differences in health outcomes among clusters. We next summarize the findings for each cluster. About 18% of those studied reported a Healthy eating pattern, with generally good nutrition and health behaviors. Their baseline health status was good, except for a higher prevalence of heart disease, cancer, and arthritis, possibly the reason that some chose the Healthy diet. (More in that cluster reported being on a medically prescribed diet.) Cluster 5 excelled in YOL and YHL, with or without control for baseline health variables. However, it also had significantly more angina and MIs than Cluster 4, even after adjustment for the excess in heart disease at baseline, which was not expected. Persons in the Unhealthy diet cluster generally had poorer nutrition, poorer health behaviors, were sicker at baseline, and had worse outcomes than the Healthy diet cluster, with or without control for baseline health. The YOL and YHL outcomes were significantly worse than Cluster 5. We had hoped that Cluster 1 might represent persons on high protein/ low carbohydrate “Atkins-type” diets, for which no longterm information is available [30]. However, no one in Cluster 1 had a diet as extreme as the induction level of the Atkins diet, and poor outcomes on that diet cannot be inferred. The Low Calorie and High Calorie clusters had about the expected intake of fat, fiber, protein, and carbohydrates, but differed greatly in their calorie intake. Outcomes for the Low-Calorie cluster were similar to those for the Healthy diet cluster, and those for the High-Calorie Cluster more like the Unhealthy diet cluster. Taken at face value, this would suggest that a lower calorie diet with the expected level of nutrients is better than a similar higher calorie diet for older adults. However, the two clusters differed on many other features, which also may explain their different outcomes. Furthermore, as mentioned above, food-frequency questionnaires are believed to provide poor estimates of total energy intake, as supported by the lack of difference in BMI in the two groups. Because the nutrition data did not include portion size, another difference between these groups may be the variety of foods eaten, which can be confused with energy intake. Cluster 2 had more favorable covariates than Cluster 1, but the outcomes were never significantly different. Cluster 3 had favorable covariates but not as favorable as Cluster 5 (except for cancer). The outcomes were never significantly different. The interpretation of Clusters 2 and 3 is unclear, and they may simply be less extreme versions of the Unhealthy and Healthy diet clusters, respectively. The positive outcomes for angina and MI in the Low 4 cluster were unexpected, because the cluster had less favorable demographics and health behaviors than the Healthy

diet cluster. Cluster members were relatively healthy at baseline (Table 4), but they had poor nutrition (Table 2). Low 4s most distinctive nutritional characteristic was higher alcohol consumption. However, only 26% in Cluster 4 reported drinking any alcohol (compared to 12% in the other clusters). Mean drinks per week was 4.8 (vs. 2.4 in other clusters), and mean drinks per drinker was 8.5 (vs. 4.0). Of the 41 persons reporting 25 or more drinks a week, 23 (56%) were in Cluster 4. Higher alcohol use is thus a characteristic of the cluster, but not of all its members. Low 4s averaged only 1,583 calories per day, and were below average on vitamins and minerals. Although they averaged only five drinks a week, this was a substantial component of their total caloric intake. Their diet was also high in relative fat. Macfarlane referred to a similar dietary pattern as “cholesterol on the rocks” in the context of it being beneficial with respect to heart disease risk [34]. Cluster 4 was significantly worse than Cluster 5 only for YHL. Cluster 4 had more deaths than Cluster 5 due to cancer and respiratory problems (based on unadjudicated data—not shown), which is consistent with their higher smoking rate. Because Cluster 4 had no survival advantage, it is possible that the relatively good cardiovascular outcomes were related to competing risks from cancer and respiratory problems; that is, they may have had less heart disease because they died of something else first. More detailed analyses focusing on alcohol use and cardiovascular disease are underway in CHS, and may shed more light on these interesting findings. 4.1. Literature Because we defined dietary patterns using cluster analysis, there is no strictly comparable literature. There have been other cluster analyses of dietary factors, with the clusters then compared to other nutritional variables, or cross-sectionally to health behaviors and health factors, similar to our Tables 2–4. These were based on cluster analysis of food groups [5–11], nutrient groups [12,13], and micronutrient groups [14]. The number of variables clustered ranged from 6 to 74, and the number of reported clusters ranged from 4 to 8. The clusters were never assessed using longitudinal health outcomes. Some articles have considered the theoretical consequences of popular diets [32,35], but have not followed individuals to see actual consequences. There have been other attempts to characterize diet in a more comprehensive way. The healthy eating index (HEI) [36] and the related dietary adequacy index [3], for example, were created to characterize adherence to dietary guidelines that were established by panels of experts to reduce risk of major chronic diseases including cardiovascular disease and cancer. The HEI performed well in terms of capturing dietary variety, fruit intake, and fat intake [4]. However, it was not predictive of these disease outcomes in the Nurses Health Study cohort [37], and only a weak association was found in the Male Health Professional Study [38]. Similarly, in our study

P. Diehr, S.A.A. Beresford / Journal of Clinical Epidemiology 56 (2003) 1224–1235

the cardiovascular outcomes of the Healthy diet cluster were not the best, although their YOL and YHL were superior. Our study differs from these two studies in that the clusters represent observed rather than hypothetical dietary patterns, as well as in the older age of participants. In a somewhat related longitudinal study, Fung et al. factor-analyzed information on 38 food groups for women aged 38 to 63 [39]. They identified two factors that they labeled the Prudent and Western diets. The Prudent diet was associated with less coronary heart disease and the Western diet with more. Factor analysis, which groups together variables that are highly correlated, often has results similar to cluster analysis, which groups people with similar features. Although the methods and the study populations are very different, the two factors may be similar to our Healthy and Unhealthy patterns, with somewhat similar outcomes. 4.2. Limitations There are some limitations to these findings. They have to do with the nature of the study, the process of forming clusters, data quality, and the limited number of analyses we could present. The clustering process is not definitive, and different results might have emerged if we had chosen different clustering variables, a different numbers of clusters, or perhaps a different clustering algorithm. The cluster solution we presented had five interesting dietary patterns, and so served our purposes. The nutrition data used here are a unique resource, providing information about older adults with 10-year follow-up on important outcomes. However, food-frequency questionnaires are subject to differential reporting errors that may correlate with characteristics such as body mass index or quantity of food eaten [20]. Because portion size was not measured, some individuals who ate small portions of a wide variety of foods may have been misclassified as high calorie, while some who ate large portions of a smaller number of foods were misclassified as low calorie. We believe that the relationships among the nutrients are characterized well, but that the absolute level of calories and other nutrients is in question. The clusters were based on relationships among the relative nutrients, and so should be robust to this type of error. Even if the data accurately measure the diet at baseline, persons may not have followed a similar pattern in the years before or after this assessment, making it difficult to ascribe health effects to diet. We showed that there was reasonable persistence over time of dietary patterns. If people chose their diet because of health problems, it would be inappropriate to ascribe the health problems to the diet. In the analysis we adjusted, through regression, for a large set of baseline health problems, to account for this possibility. Such adjustment had little effect on the outcome findings. Problems with the data or lack of persistence could have caused the clusters to be based on “noise” rather than on diet. In the worst case, if the dietary data were no more

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than random, we could still have defined five clusters, and probably even been able to “interpret” them. We would probably not have found significant differences among the clusters on the other variables, however. Because we found many such differences among clusters, problems with the data may not have been a major factor. Because this is an observational study, we cannot make causal statements. Although we adjusted for a wide number of health behaviors and baseline health problems, there may be unmeasured reasons for dietary choice that are more important than diet as predictors of these outcomes. The emphasis on clusters allowed us to study dietary patterns, but did not identify individual nutrients that were associated with future health. For example, the poorer outcomes associated with Cluster 1 might naively be thought to reflect the effects of a diet high in fat. Such a conclusion is inappropriate, because it is the pattern of nutrients rather than a single nutrient that has shown such an association. If we define high fat intake arbitrarily to be at the 80th percentile of the observed data, only 35% of the high absolute fat eaters are in Cluster 1, and 49% are in Cluster 2. With respect to relative fat, 48% of the high fat eaters are in Cluster 1, and 43% are in Cluster 4. Thus there is not a one-to-one match between high consumption of fat and cluster membership, and the reader should not infer associations with individual nutrients from the findings at the cluster level. Finally, because of space limitations and the large number of variables investigated, we were unable to report in detail on the relationship of most of the variables to the clusters. We compared each cluster to Cluster 5, but did not report on all comparisons among the other clusters. We hope that this work will generate hypotheses for future research.

5. Conclusion In a population of older adults, about 18% were found to follow what is generally considered to be a healthy eating pattern, and which was associated with better nutrition, better health behaviors, and often with better baseline health. This cluster had better YOL and YHL than the other clusters, but cardiovascular outcomes were significantly better in a different cluster whose diet was not similar to the “healthy” diet in any sense. The Unhealthy diet cluster, whose diet was relatively high in protein and fat and relatively low (although not absolutely low) in carbohydrates and fiber generally had worse baseline and future health, and cannot be recommended.

Acknowledgments The research reported in this article was supported by contracts N01-HC-85079–N01-HC-85086 from the National Heart, Lung, and Blood Institute, and RC-HL 35129 and RC-HL 15103. Participating Institutions and Principal Staff:

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Forsyth County, NC—Wake Forest University School of Medicine: Gregory L. Burke, Sharon Jackson, Alan Elster, Curt D. Furberg, Gerardo Heiss, Dalane Kitzman, Margie Lamb, David S. Lefkowitz, Mary F. Lyles, Cathy Nunn, Ward Riley, John Chen, Beverly Tucker. Forsyth County, NC—Wake Forest University, ECG Reading Center: Farida Rautaharju, Pentti Rautaharju. Sacramento County, CA— University of California, Davis: William Bonekat, Charles Bernick, Michael Buonocore, Mary Haan, Calvin Hirsch, Lawrence Laslett, Marshall Lee, John Robbins, William Seavey, Richard White. Washington County, MD—The Johns Hopkins University: M. Jan Busby-Whitehead, Joyce Chabot, George W. Comstock, Adrian Dobs, Linda P. Fried, Joel G. Hill, Steven J. Kittner, Shiriki Kumanyika, David Levine, Joao A. Lima, Neil R. Powe, Thomas R. Price, Jeff Williamson, Moyses Szklo, Melvyn Tockman. Washington County, MD—The Johns Hopkins University, MRI Reading Center: Norman Beauchamp, R. Nick Bryan, Douglas Fellows, Melanie Hawkins, Patrice Holtz, Naiyer Iman, Michael Kraut, Cynthia Quinn, Grace Lee, Carolyn C. Meltzer, Larry Schertz, Earl P. Steinberg, Scott Wells, Linda Wilkins, Nancy C. Yue; Allegheny County, PA—

University of Pittsburgh: Diane G. Ives, Charles A. Jungreis, Laurie Knepper, Lewis H. Kuller, Elaine Meilahn, Peg Meyer, Roberta Moyer, Anne Newman, Richard Schulz, Vivienne E. Smith, Sidney K. Wolfson. University of California, Irvine—Echocardiography Reading Center (baseline): Hoda Anton-Culver, Julius M. Gardin, Margaret Knoll, Tom Kurosaki, Nathan Wong. Georgetown Medical Center—Echocardiography Reading Center (follow-up): John Gottdiener, Eva Hausner, Stephen Kraus, Judy Gay, Sue Livengood, Mary Ann Yohe, Retha Webb. New England Medical Center, Boston—Ultrasound Reading Center: Daniel H. O’Leary, Joseph F. Polak, Laurie Funk. University of Vermont—Central Blood Analysis Laboratory: Elaine Cornell, Mary Cushman, Russell P. Tracy. University of Arizona, Tucson—Pulmonary Reading Center: Paul Enright. University of Washington, Seattle—Coordinating Center: Alice Arnold, Annette L. Fitzpatrick, Richard A. Kronmal, Bruce M. Psaty, David S. Siscovick, Will Longstreth, Patricia W. Wahl, David Yanez, Paula Diehr, Corrine Dulberg, Bonnie Lind, Thomas Lumley, Ellen O’Meara, Jennifer Nelson, Charles Spiekerman. NHLBI Project Office: Robin Boineau, Teri A. Manolio, Peter J. Savage, Patricia Smith.

Appendix Descriptive statistics for all nutrition variables, by gender Women

Fat (g) daily Dietary Fiber (g) Carbohydrate (g) Protein (g) Kilocalories Retinol intake Vitamin a (IU) Thiamin (mg) Riboflavin (mg) Niacin (mg) Vitamin C (mg) Calcium (mg) Iron (mg) Potassium (mg) Phosphorus (mg) Sodium (mg) Saturated Fat (g) Cholesterol (mg) Oleic Acid (g) Linolec Acid (g) Kcal from Alcohol

Men

N

Mean

Standard deviation

N

Mean

Standard deviation

2634 2634 2634 2634 2634 2631 2634 2633 2633 2634 2634 2634 2634 2634 2634 2634 2634 2634 2634 2634 2634

64.99 18.64 206.29 77.39 1726.91 1092.47 14,028.97 1.42 2.11 20.29 209.82 852.23 14.64 3251.64 1297.96 3105.97 22.81 305.85 22.88 12.51 185.15

32.18 7.22 75.37 34.49 669.80 971.97 8477.86 .61 1.03 9.33 97.10 366.54 6.64 1156.00 526.61 1441.05 12.52 194.89 12.41 6.25 523.58

1976 1976 1976 1976 1976 1973 1976 1974 1976 1976 1975 1976 1976 1976 1976 1976 1976 1976 1976 1976 1975

76.16 17.75 216.92 84.58 1928.03 1232.68 13,650.56 1.52 2.26 22.39 185.90 868.32 15.72 3309.95 1392.53 3392.02 27.61 366.81 27.64 14.00 439.06

36.13 7.11 77.66 36.02 722.40 1104.33 8643.48 .65 1.09 9.88 90.41 370.23 6.89 1169.49 540.77 1520.88 14.27 220.44 14.17 6.93 956.58

References [1] U.S. Department of Agriculture and U.S. Department of Health and Human Services. Nutrition and your health: dietary guidelines for Americans. Home and Garden Bulletin no. 232. Beltsville (MD): Author; 2000. [2] Healthy Persons 2000. Nutrition objectives. J Am Diet Assoc 1991; 91:1515–6. [3] Haines PS, Siega-Riz AM, Popkin BM. The diet quality index revised: a measurement instrument for population. J Am Diet Assoc 1999;99: 697–704.

[4] Hann CS, Rock CL, King I, Drewnowski A. Validation of the Healthy Eating Index with use of plasma biomarkers in a clinical sample of women. Am J Clin Nutr 2001;74:479–86. [5] Akin JS, Guilkey DK, Popkin BM, Fanelli MT. Cluster analysis of food consumption patterns of older Americans. J Am Diet Assoc 1986;86:616–24. [6] Boeing H, Klein U, Hendrichs A. Strategies for analysing nutritional data for epidemiological purposes—food patterns by means of clusteranalytical procedures. Nutr Rep Int 1989;40:189–98.

P. Diehr, S.A.A. Beresford / Journal of Clinical Epidemiology 56 (2003) 1224–1235 [7] Tucker KL, Dallal GE, Rush D. Dietary patterns of elderly Bostonarea residents defined by cluster analysis. J Am Diet Assoc 1992; 92:1487–91. [8] Wirfalt AKE, Jeffery RW. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J Am Diet Assoc 1997;97:272–9. [9] Greenwood DC, Cade JE, Draper A, Barret JH, Calvert C, Greenhalgh A. Seven unique food consumption patterns identified among women in the UK Women’s Cohort Study. Eur J Clin Nutr 2000;54:314–20. [10] Millen BE, Quatromoni PA, Copenhafer DL, Demissie S, O’Horo CE, D’Agostino RB. Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies. J Am Diet Assoc 2001;101:187–94. [11] Haveman-Nies A, Tucker KL, de Groot CPGM, Wilson PWF, van Staveren. Evaluation of dietary quality in relationship to nutritional and lifestyle factors in elderly people of the US Framingham Heart Study and the European SENECA study. Eur J Clin Nutr 2001; 55:870–80. [12] Hulshof KFAM, Wedel M, Lowik MRH, Kok FJ, Kistemaker C, Hermus RJJ, ten Hoor F, Ockhuizen T. Clustering of dietary variables and other life-style factors. J Epidemiol Community Health 1992; 46:417–24. [13] Huijbregts PPCW, Feskens EJM, Kromhout E. Dietary patterns and cardiovascular risk factors in elderly men: the Zutphen elderly study. Int J Epidemiol 1995;24:313–20. [14] Schroll K, Carbajal A, Decarli B, Martins I, Grunenberger F, Blauw YH, de Groot CPGM. Food patterns of elderly Europeans. Eur J Clin Nutr 1996;50(Suppl 2):S86–100. [15] Fried LP, Borhani NO, Enright PL, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991;1:263–76. [16] Ives G, Fitzpatrick A, Bild D, et al. Surveillance and ascertainment of cardiovascular events: the cardiovascular health study. Ann Epidemiol 1995;5:278–85. [17] Kumanyika S, Tell GS, Fried LP, Martel JK, Chinchilli VM. Picture sort method for administering a food frequency questionnaire to older adults. J Am Diet Assoc 1996;96(2):137–44. [18] Willett W. Nutrional epidemiology. 2nd ed. New York: OUP; 1998. [19] Kumanyika S, Tell GS, Shemanski L, Martel J, Chinchili VM. Dietary assessment using a picture sort approach. Am J Clin Nutr 1997; 65:1123S–9S. [20] Kumanyika S, Tell GS, Shemanski L, Polak JF, Savage PJ. Eating patterns of community-dwelling older adults: the Cardiovascular Health Study. Ann Epidemiol 1994;4:404–15. [21] Siscovick DS, Fried L, Mittelmark M, Rutan G, Bild D, O’Leary DH. Exercise intensity and subclinical cardiovascular disease in the elderly. The Cardiovascular Health Study. Am J Epidemiol 1997;145:977–86.

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[22] Orme J, Reis J, Herz E. Factorial and indiscriminate validity of the Center for Epidemiological Studies Depression (CES-D) scale. J Clin Psychol 1986;42:28–33. [23] Psaty BM, Kuller LH, Bild D, et al. Methods of assessing prevalent cardiovascular disease in the Cardiovascular Health Study. Ann Epidemiol 1995;5:270–7. [24] O’Leary DH, Polak JF, Wolfson SK Jr, et al. The use of sonography to evaluate carotid atherosclerosis in the elderly: The Cardiovascular Health Study. Stroke 1991;22:1155–63. [25] Radoff LL. The CESD Scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1:385–401. [26] Cushman M, Cornell ES, Howard PR, et al. Laboratory methods and quality assurance in the Cardiovascular Health Study. Clin Chem 1995;41:264–70. [27] Diehr P, Patrick DL, Burke GL, Williamson J. Survival versus years of healthy life: which is more powerful as a study outcome? Control Clin Trials 1997;20:267–79. [28] Diehr P, Patrick DL, Bild DE, Burke GL, Williamson JD. Predicting future years of healthy life for older adults. J Clin Epidemiol 1998; 51:343–53. [29] Willett WC, Stempfer MJ. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol 1986;124:17–27. [30] Atkins RC. Dr. Atkins new diet revolution. New York: Avon Books; 1992. [31] U.S. Department of Health and Human Services. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. Bethesda (MD): U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute; 1998. [32] Anderson JW, Konz EC, Jenkins DJ. Health advantages and disadvantages of weight-reducing diets: a computer analysis and critical review. J Am Coll Nutr 2000;19:578–90. [33] Boykin LS. Soul foods for some older Americans. J Am Geriatr Soc 1975;23:380–2. [34] McFarlane D. Letter to the editor. N Engl J Med 1985;313(2):118–20. [35] Porcello LA. A practical guide to fad diets. Clin Sports Med 1984;3:723–9. [36] Kennedy ET, Ohls F, Carlson S, Fleming K. The healthy eating index: design and applicaitons. J Am Diet Assoc 1995;95:1103–8. [37] McCullough ML, Feskanich D, Stampfer MJ, Rosner BA, Hu FB, Hunter DJ, Variyam JN, Colditz GA, Willett WC. Adherence to the Dietary Guidelines for Americans and risk of major chronic disease in women. Am J Clin Nutr 2000;72:1214–22. [38] McCullough ML, Feskanich D, Rimm EB, Giovannucci EL, Ascherio A, Variyam JN, Spiegelman D, Stampfer MJ, Willett WC. Adherence to the Dietary Guidelines for Americans and risk of major chronic disease in men. Am J Clin Nutr 2000;72:1223–31. [39] Fung TT, Willett WC, Stampfer MJ, Manson JE, Hu FB. Dietary patterns and the risk of coronary heart disease in women. Arch Intern Med 2001;161:1857–62.