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Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111
2To whom correspondence should be addressed. E-mail: katherine.tucker{at}tufts.edu.
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • Dietary patterns • cluster analysis • elderly • Hispanic • Puerto Rican
Many studies have examined the association between the intake of individual nutrients or foods and the risk of chronic disease, but only recently has attention focused on the relationship of overall dietary patterns to health risk. Conceptually, people do not eat isolated nutrients, but rather meals composed of a variety of foods, with complex combinations of nutrients that may interact (1). Dietary pattern analysis is useful because it provides a basis from which to make recommendations on eating practices (2). In clinical studies, changes in dietary patterns appear to lower blood pressure more effectively than single-nutrient supplementation (3,4).
The identification of eating patterns that may contribute to the current epidemic of obesity is of great importance. However, how dietary patterns may contribute to obesity, particularly among ethnic minority groups, is little understood. Hispanics comprise the largest minority population in the United States (5), and the Hispanic population continues to grow rapidly (6). We have previously shown that elderly Hispanics living in Massachusetts have a higher prevalence of obesity (7), diabetes (8), systolic hypertension (9) and physical disability (10) than a neighborhood-based sample of non-Hispanic white elders. We have also noted that the diets of these Hispanic elders tend to be high in refined grains (11).
Diets high in refined grains may affect the development of insulin resistance (12). Conversely, improvements in cardiovascular health and healthier body weights have been observed among those consuming diets based on fruit and vegetables, low fat foods or nutrient-dense foods (13). In the Baltimore Longitudinal Study of Aging, a dietary pattern characterized by elevated consumption of fruit, vegetables, low fat dairy products and whole grains was associated with smaller gains in BMI and waist circumference (14).
Individual dietary patterns reflect interactions of biology, culture, food availability, education and lifestyle. Despite shared ancestry and common language, the U.S. Hispanic population has a diversity of dietary traditions and eating practices. Moreover, because of variation in time of migration, there is an ongoing transition from traditional to more "Americanized" diets. The current study, therefore, was designed to examine the major dietary patterns of Puerto Rican and Dominican elders, the two most common Hispanic groups living in Massachusetts, compared with non-Hispanic white elders; to identify how acculturation is associated with dietary patterns; and to assess the association among those dietary patterns with total and central obesity.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The sampling of the subjects for this study has been described elsewhere (8). In brief, 1030 subjects aged 60 to 92 y participated in the Massachusetts Hispanic Elderly Study (MAHES), a statewide survey conducted between 1993 and 1997 with a representative sample of Hispanic elders. For this analysis, we included 449 Hispanics from Puerto Rico and 133 from the Dominican Republic, along with 243 non-Hispanic whites selected from the same neighborhoods. Because they were neighborhood based, the socioeconomic indicators for the non-Hispanic white elders were closer to those of the population of Hispanic elders than would have been seen with a random sample of non-Hispanic whites (8). The remaining 205 subjects belonged to other Hispanic groups (Cubans, Mexican-Americans, Central and South Americans) with diverse customs and numbers too small to draw meaningful conclusions. Therefore, they were excluded from this analysis. Subjects with implausible energy intakes (<2.51 or >16.74 MJ; n = 52) were also excluded. No subject was excluded for health reasons. The study was conducted with the approval of the Institutional Review Board at Tufts–New England Medical Center in Boston, and all subjects gave informed consent.
Assessment of dietary patterns.
Usual dietary intake was assessed with a specially designed 118-item food frequency questionnaire (FFQ) (15). The FFQ reference period was the preceding three months. The FFQs were scanned (Opscan5, National Computer Systems, St. Paul, MN) and transferred to electronic files. The Minnesota Nutrient Data System, program 2.8, version 25 (NDS, University of Minnesota, Minneapolis, MN) was used to calculate the nutrient intake profiles. Intake from dietary supplements, obtained through a detailed inventory of the products reported by the study subjects, was also included on the FFQ. We created a dichotomous variable (yes/no) for dietary supplement use for use in this analysis.
The 118 food items in the FFQ were collapsed into 32 food groups (see Appendix). Foods were categorized by nutrient profile, cooking method and frequency of use. For example, we retained rice as an independent group because it is a staple food in Caribbean Hispanic diets. Individual energy intake contribution was calculated for each of the 32 food groups and used for the cluster analysis. Standardization as percent energy allowed description of food choices proportional to intake while avoiding biased grouping due to variation in body size and energy requirement.
We performed cluster analysis to group study subjects by similarity of eating patterns, using SAS PROC FASTCLUS (SAS version 8.0, SAS Institute, Cary, NC). This procedure performs a disjoint cluster analysis on the basis of Euclidean distances computed from contributing variables. Cluster seeds are first assigned at approximate cluster locations. The Euclidean distance from each subject to each cluster center is calculated, and the subject is assigned to the nearest cluster center. The seeds are then replaced within the revised clusters, and the distance calculation and assignment are repeated in an iterative process until no further changes occur.
Cluster analysis is sensitive to extreme outliers. Energy contribution values that were 
5 SD from the mean of a food group variable were removed from the clustering analysis, as has been done previously (14,16). Outliers were also identified if there were fewer than five members in a cluster (17). Applying both criteria, a total of 108 outliers (13.1%) were identified and removed temporarily. The remaining 717 eligible subjects were used for the overall cluster generation. Once the set of cluster centers was determined, the outliers were added back into the analysis and assigned to the cluster centers closest to their variable set.
A series of cluster analyses, with 3 to 8 clusters specified, was performed to identify the most meaningful set of patterns (18). The ratio of between- and within-cluster variance for each set of clusters was examined to assess group separation (16). A five-cluster scheme was selected for its distinct dietary composition and nutritional implications. These five clusters were identified as sweets, whole milk, rice, starchy vegetables, and fruit and breakfast cereals, after the food groups that contributed relatively higher proportions of dietary energy to each cluster relative to the others.
Sociodemographic and lifestyle data.
Data on age, education, income, family size, self-reported level of physical activity and cigarette smoking were collected by standardized questionnaire for all subjects. Smoking was dichotomized as current smoker versus nonsmoker. Alcohol use was dichotomized as current drinker versus nondrinker. An income/poverty ratio was calculated based on household income and the poverty threshold specified by the U. S. Census Bureau, which varied with the number of persons in the family, the age of the reference person and the month and year in which the family was interviewed (19). A cutoff of 1.0 was used to classify subjects as above or below this poverty threshold.
Acculturation.
Acculturation data for Hispanics were obtained with a set of questions on the usage of English and/or Spanish for speaking, reading and writing. We constructed an acculturation score as the mean of these three language variables, each with a range of 1.0 (using only Spanish) to 5.0 (using only English) (11).
Obesity measures.
Anthropometric measurements included weight (kg), height (m) and waist circumference (cm). General obesity was measured with the body mass index (BMI, kg/m2), calculated from height and weight. Height of those unable to stand and those with stooped posture (30% of subjects) was estimated from knee height, using published equations developed for Caribbean Hispanics and for non-Hispanic whites (20,21). Central obesity was measured as the waist circumference.
Statistical analysis.
Comparisons across individuals in the five dietary pattern groups were tested using general linear models for continuous variables and logistic regression models for categorical variables, with adjustment for age and sex. SAS (version 8.0, SAS Institute) was used for all analyses. Significance tests were two-sided with the level of significance set at P < 0.05.
The association of ethnicity with each of the dietary patterns was determined by multiple logistic regression analysis. For Hispanics only, logistic regression models were used to estimate the association between acculturation score and each dietary pattern (vs. others), adjusting for sex, age, family size and smoking status. Associations with central and total obesity were similarly tested, by adding waist circumference and BMI, respectively, to these models.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Subjects in the rice cluster obtained high percentages of total energy from rice (23%); added fats, mainly cooking oil (12%); beans (7%) and poultry (8%). Subjects in this cluster obtained the lowest energy contributions from breakfast cereal, dairy products, sweets, fruits, eggs, processed meats, fish, sweet baked goods, breads and potatoes of any dietary pattern.
Subjects in the milk cluster obtained a relatively large percentage of total energy from whole milk (>20%). Rice also contributed substantially to the total energy intake. Subjects obtained relatively low contributions from low fat milk, pasta, vegetables, meat and alcohol.
Subjects in the sweets cluster obtained relatively high contributions of total energy from grain products, including baked sweets, bread and pasta. Subjects also obtained relatively high contributions from meat, potatoes, candy and sugars, dairy desserts, processed meat, eggs, and alcohol. Subjects in this cluster obtained the lowest energy contributions from beans, starchy vegetables, poultry and rice of any dietary pattern.
Nutrient intake by dietary pattern.
Total fat intake as a proportion of energy intake was significantly higher in the whole milk (33.8%) and sweets (35.1%) dietary patterns, compared with the fruit and cereal pattern (28.2%) (Table 2). Saturated fat intake as a proportion of energy intake was higher in the whole milk (13.1%) and sweets (12.5%) patterns and lower in the starchy vegetables (8.5%) and rice (8.6%) patterns, compared with the fruit and cereal pattern (10.1%; P < 0.001). Average protein intake as a proportion of energy intake was highest in the fruit and cereal pattern (17.3%; P < 0.001), compared with other dietary patterns (15.4 to 16%).
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Ethnicity and dietary patterns.
All subjects consuming the rice dietary pattern were Hispanic, including 26% of the Puerto Rican and 39% of the Dominican samples (Table 3). Dominicans were equally divided between the starchy vegetables and rice patterns. Puerto Ricans were more evenly distributed across three patterns: rice, starchy vegetables and milk. Hispanics were less likely than non-Hispanic whites to consume the fruit and cereal dietary pattern (14.5% of Puerto Ricans and 10.5% of Dominicans vs. 37.5% of non-Hispanic whites; P < 0.001). A majority (56%) of the non-Hispanic white subjects consumed the sweets dietary pattern.
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Hispanic subjects in the sweets dietary cluster were oldest, followed by the Hispanics in the whole milk cluster; both of these groups were significantly older than Hispanics in the fruit and cereal cluster after adjusting for ethnicity (Puerto Rican vs. Dominican) and sex (P < 0.05) (Table 4). Women comprised 71% of subjects in the fruit and cereal dietary cluster, greater than the proportion of women in the starchy vegetables (56%, P < 0.05) and sweets (53%, P < 0.001) clusters. Subjects in the rice dietary cluster lived in larger households than those in the fruit and cereal cluster, (2.8 and 1.8 persons, respectively; P < 0.001) and had fewer years of education (4.2 and 9.1 y, respectively; P < 0.001). A majority (>60%) of subjects in the rice and starchy vegetables clusters were below the poverty level. The fruit and cereal cluster had the lowest proportion of current smokers (17%), lower than the sweets pattern (28%, P < 0.05); the whole milk cluster had the lowest proportion of current alcohol drinkers (13%), lower than the sweets cluster (26%).
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Hispanic elders consuming the rice dietary pattern had greater BMI (Fig. 2) and waist circumference (Fig. 3) scores, compared with the other groups. The BMI was greater for the rice cluster than for other clusters [OR = 1.05 (1.02 to 1.09), P < 0.05]. In contrast, those consuming the milk dietary pattern had lower BMI scores compared with the other groups [OR = 0.95 (0.91–0.99), P < 0.05]. Subjects in the rice dietary cluster also had greater waist circumference scores than those in the fruit and cereal cluster (100 and 97 cm, respectively; P < 0.05). Waist circumference scores remained greater in the rice cluster in multivariate models [OR = 1.03 (1.01 to 1.04), P < 0.05].
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Dietary patterns are highly determined by cultural influences on perceptions, beliefs and attitudes about foods. Such cultural constructs may not always be congruent with scientific knowledge regarding the safety or nutritional value of foods (27). As people from more traditional societies enter a new community with greater food availability, there is often a "nutrition transition," with assimilation of the mainstream culture’s dietary patterns. In the case of the mainstream diet in the United States, this generally means more total and saturated fatty acids, and less complex carbohydrates (11,28).
Earlier food consumption studies with Puerto Rican subjects showed that rice, beans and starchy roots ("viandas") constitute the dietary staples of this population group (29,30). We found that the acculturation scores of Hispanic immigrants (Puerto Ricans and Dominicans) differed significantly across dietary patterns; those who consumed the fruit and breakfast cereal dietary pattern were most acculturated. The rice dietary pattern consisted mainly of rice, beans, poultry and oil, all traditional Caribbean Hispanic foods. Not surprisingly, Hispanic elders who consumed this traditional diet tended to be less acculturated than those who consumed the other food patterns.
Recognition of the influence of cultural factors on dietary patterns is important in better understanding the factors that both enhance and inhibit consumption of healthy diets among Hispanic elders. The diets of more acculturated elderly Hispanics in the United States tend to more closely resemble the macronutrient profile of non-Hispanic whites than do the diets of those less acculturated (11). A number of studies have examined diet and acculturation in Mexican Americans. Mexican American women born in the United States, or those more acculturated to the United States, adopted more atherogenic diets (e.g., more fat and less fiber) than women born in Mexico or less acculturated to the United States (31,32). Furthermore, first-generation Mexican American women had significantly higher intakes of protein, calcium, vitamin A, vitamin C and folic acid than women in the second generation (33).
The association of the rice dietary pattern with total and central obesity, as defined by larger BMI and waist circumference scores, respectively, was an important finding of the present study. The rice dietary pattern was high in refined grains, with rice as the main source of energy. This diet was also relatively high in fat (cooking oils were the second main source of energy), which may predispose elders to obesity. Furthermore, individuals grouped in the rice dietary pattern consumed few fruits and vegetables in the present study. Dietary patterns that emphasize fruit and vegetables may have beneficial effects on cardiovascular health and have been associated with healthier body weights (13).
Causality between the obesity indicators and consumption of the rice dietary pattern could not be determined because of the cross-sectional nature of this study. However, significant associations with both BMI and waist circumference remained after adjusting for age, sex, poverty, education, family size and acculturation level. Other studies have also identified associations between dietary patterns and obesity. In one study of over 500 adults, those consuming a soft drink dietary pattern had higher BMI scores than those who consumed skim milk or meat and cheese patterns (18). Using factor analysis to determine food patterns, Maskarinec found a significant association between obesity and a meat-based dietary pattern in a multiethnic group of women (34). Recently, a dietary pattern high in fruit, vegetables, low fat dairy and whole grains was associated with smaller 3-y gains in BMI and waist circumference among adults, compared with other dietary patterns (14).
The known health risks associated with total obesity, and particularly with abdominal obesity, are well documented (35,36). Obesity affects physical functioning (37), impairs quality of life (38), increases mortality (39) and elevates use of health care resources (40). Increasing proportions of older adults are overweight or obese (41). We previously reported a high prevalence of total and central obesity in this study population of Hispanic elders (7).
Abdominal obesity, characterized by accumulation of visceral fat mass and insulin resistance, is followed by metabolic abnormalities that are risk factors for cardiovascular disease, diabetes and stroke (42). Body composition influences the response of the hypothalamic-pituitary-adrenal axis to meals with different carbohydrate content, and it has been suggested that the hyperactivation that occurs with the intake of large amounts of carbohydrates may contribute to insulin resistance and associated hyperinsulinemia, particularly in women with abdominal obesity (43,44).
In conclusion, the majority of the Puerto Rican and Dominican elders in this study group consumed diets with relatively large energy contributions from either rice or starchy root crops. Those with higher acculturation scores appeared to adopt a more diverse and nutrient-rich fruit and breakfast cereal diet. The association of the rice dietary pattern with obesity suggests a need for further research on the consequences of this dietary pattern and for the development of culturally appropriate dietary interventions for this rapidly growing subset of the U.S. population.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Manuscript received 25 June 2003. Initial review completed 26 July 2003. Revision accepted 8 September 2003.
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LITERATURE CITED |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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