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Nutritional Epidemiology

Adults with Healthier Dietary Patterns Have Healthier Beverage Patterns^1,2

Department of Nutrition, School of Public Health, University of North Carolina, Chapel Hill, NC 27516

^* To whom correspondence should be addressed. E-mail: popkin{at}unc.edu.

	ABSTRACT

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
There is an absence of research examining associations between food and beverage intake patterns and most research has centered on soft drinks, whereas research on overall beverage patterns is absent. Using data from the National Health and Nutrition Examination Survey 99–02 for adults aged 19 y and older, we independently examined beverage and food intake patterns, as well as their interrelations. Cluster analysis generated mutually exclusive intake patterns for beverages and foods. Multinomial logistic regression models provided the odds of a given beverage pattern for each food pattern; we then compared the probability of a given beverage pattern for each food pattern. Six beverage and 6 food patterns emerged. Beverage patterns revealed that calorically sweetened, noncaloric, and diet beverages tended to be consumed independently of one another. Being in the Snacks and High-Fat Foods cluster increased the odds of being in the Coffee and Soda (odds ratio (OR): 1.62 [95% CI: 1.27–2.06]) or Nutrients and Soda (OR: 1.51 [95% CI: 1.14–2.00]) beverage clusters and decreased the odds of being in the Water and Tea (OR: 0.51 [95% CI: 0.52–0.97]) cluster relative to the odds of being in the Water, Coffee, and Tea cluster. The opposite was true for the Vegetable pattern. Furthermore, persons who had a healthier food pattern had a higher probability of having a noncaloric beverage pattern than persons who did not. Increasing awareness of both the contribution of calorie-containing beverages to overall energy intake and dietary patterns associated with these beverages helps inform policies targeted at reducing energy intake in the population.

	Introduction

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

Numerous clinical studies report very little adjustment in food intake when beverages are consumed; beverages have weak satiety properties and elicit poor dietary compensation (12–16). Recent reviews, including the Institute of Medicine Panel on Water and Electrolytes, the Beverage Guidance Panel, and the U.S. Dietary Guidelines 2005 Panel noted excessive added sugar in the U.S. diet from calorically sweetened beverages (6,17,18). Fluid consumption is an essential contributor to water balance, as only 20% of U.S. fluid needs are provided by food sources. However, among adults 19 y and older, the contribution of fluids to meeting the recommended daily allowances (RDAs) for essential nutrients is minimal, except for milk (28% RDA calcium; 9% vitamin A and potassium) and vegetable and fruit juices (23% RDA vitamin C) (18). This balance between energy and nutrient content from beverage represents a critical component in delineating the role of beverages in a healthy diet, as recently addressed by the Beverage Guidance Panel's publication on this topic (6).

In light of the absence of research examining the associations between relative contributions of beverages and foods to total energy intake, this paper builds on the current body of literature by attempting to link the two. This paper examines current beverage intake among U.S. adults by defining distinct clusters of beverage consumers, describes the sociodemographic factors associated with these beverage clusters, and explores associations between particular beverage and food clusters.

	Methods

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
    Study sample. The NHANES design is a stratified, multistage probability sample of the civilian, noninstitutionalized U.S. population (19). This study used data from individuals 19 y and older with completed dietary and medical examination components of the 1999–2002 surveys, thus providing a valid survey weight for the 4-y period. A final sample of 9491 individuals was used in this analysis.

    Measurement of dietary data. The objective of the NHANES dietary component was to obtain detailed dietary consumption information. Each participant completed a single 24-h recall. Details of the NHANES computer assisted dietary interview system, probes, response options, and food composition table are provided elsewhere (20). The computer assisted dietary interview system was linked to the USDA Food and Nutrient Database for Dietary Studies, which updated information on weights and food measures for fast foods and individually sized products in 2004 (21). For a subsample of participants, we collected a second follow-up 24-h recall; however, only the first was publicly available for use with the combined NHANES 1999–2002 dataset.

    Food group descriptions. The University of North Carolina-Chapel Hill food grouping system was used to examine food and beverage consumption (22). This system aggregates foods from the USDA nutrient composition tables into 74 nutrient-based subgroups according to fat and fiber content. To generate meaningful food patterns, the number of variables used in cluster analysis must be limited. We examined various alternatives for reducing the number of food and beverage variables and ultimately grouped variables sharing behavioral consumption characteristics (i.e., combining soda and fruit drinks into a single category). Based on this work, and in a separate article (23), we found that many of the food groupings did not facilitate differentiation of beverage patterns; thus, it was most convenient to use them as a combined group, for example using a bread group instead of individual high-fat and low-fat bread groups. Also, our goal was to examine the relation between key food and beverage items that were representative of overall patterns, not to generate the most detailed food and beverage patterns possible.

In the end, we included 14 food and 9 beverage groups in analyses. These variables were selected because they were representative of both healthy and less healthy diets and accounted for all foods and beverages consumed by our sample. These variables are similar to those reported in an analysis of water and food consumption patterns (23). The food and beverage groups used in cluster development are shown in the online supplemental materials.

    Measurement of covariates. We conducted a combination of in-home and in-person interviews to gather information on demographic, socioeconomic, and anthropometric factors such as annual income (defined as percent poverty to income ratio [PIR]), education (less than high school, completed high school, more than high school), race/ethnicity (White; non-Hispanic Black; Mexican American; Other race, Hispanic; and Other race, non-Hispanic), and BMI (kilogram/meter²). Other race and Hispanic and Other race, non-Hispanic are referred to as Other Races hereafter.

    Cluster development. To inform our clusters, we examined weighted data regarding the percentage of participants consuming each beverage, amount consumed (kilojoules and milliliters), and distribution of selected demographic factors among consumers. Guided by these findings, cluster analysis was performed on an unweighted sample for beverages, treated as dichotomous (consumed yes/no) variables due to large numbers of nonconsumers, and foods, treated as continuous, standardized Z-scores, independently (SAS FASTCLUS, version 8.2; Research Triangle Institute). The purpose of cluster analysis is to place individuals into mutually exclusive groups, or clusters, as suggested by the data and not defined a priori, such that individuals in a given cluster are distinctly similar to one another and distinctly different from individuals in other clusters with respect to their mean consumption of foods and beverages. This method has been employed in previous studies of dietary patterns (for example, see 23–26).

FASTCLUS uses Euclidean distances to create cluster centroids based on least squares estimation. Optimal specifications for initial cluster centers were identified by conducting 1,000 iterations of cluster procedures, by which initial group centers were randomly generated. Iterations that produced the largest r² values indicated the best fit for the data and maximized the inter-to intra-cluster variability ratio (27).

To determine the most appropriate cluster solution, we compared cluster membership across increasingly complex cluster solutions, increasing from 3 to 8 clusters. If the more complex solution broke clusters into meaningful subgroups, this solution was favored. Additionally, to maintain within-cluster reliability, a cluster could contain no <4% of the sample. Comparing and describing the entire distribution of all beverage (or food) groups in a given cluster aided interpretation of the clusters. For foods, Z-scores of ±2.0 were considered significantly different; ±1.0 were considered as having clear differentiation; ±0.5 to ±1.0 indicated possible differentiation. Clusters were then named accordingly. For example, a cluster with food group Z-scores of 3.917 for Fruits, 0.225 for Low-Fat Dairy, and <0.100 for all other food groups is identified as a Fruit and Low-Fat Dairy food cluster. Our final solutions included 6 beverage and 6 food clusters. These represented the most robust patterns and maximized inter-cluster variability and intra-cluster correlation. Clusters were named according to 1) the beverages that contributed most to intake within a single cluster and 2) the beverages that allowed us to differentiate a single cluster from the remaining 5.

    Statistical analysis. To explore the relation between particular food and beverage clusters, we used multinomial logistic regression models, controlling for important demographic factors, with Stata 9.1. The multinomial logistic regression command fits a maximum likelihood, multinomial logistic regression model, which allows for multiple comparisons across all outcomes. To test whether there was an interaction of age with food pattern, we entered the appropriate cross-product term into the model and performed a Wald Chunk Test. As a group, the interaction terms were not statistically significant and were not included in the final model (P > 0.10).

Using the coefficients derived from the fully adjusted regression model, we used the STATA PREDICT command to evaluate, for each participant, the probability of falling into 1 of the 6 beverage clusters, given that the individual was in 1 of 6 food clusters. With categorical outcomes, the PREDICT command produces the probability of an event (outcome) based on 1) model parameters and 2) adjustments made by the researcher. For example, using the multinomial logistic regression-derived coefficients, we can estimate the probability of being in the Water and Tea or Soda beverage cluster, given that 1 was also in the Fast Food or Vegetable food cluster. We then compared the estimated probability of being in a given beverage cluster between persons who either were or were not in a given food cluster. For example, we compared the probability of being in the Soda' cluster between persons who were in the Vegetable cluster with persons who were not in this cluster. In this way, such simulations provide some sense of how changing food intake, by changing the food cluster into which the individual falls, may affect beverage consumption.

	Results

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
The final sample of adults (n = 9,491) were primarily white (47.9%), Mexican American (24.5%), and black (19.5%). Just under one-half were male (47%) and two-thirds had at least a high school education. PIR was equally distributed between low (<180% PIR; 36.9%) and high (>350% PIR; 33.9%). Overweight and obesity were prevalent at 64% of the population. Nearly one-third (29.5%) of the sample consumed >25% of their total daily energy intake from beverages, whereas 56.8% consumed 15% of daily total energy intake from beverages.

    Current consumption patterns. Water was the most commonly consumed beverage (88% of adults; Table 1), followed by coffee, soda, whole-fat milk, fruit juices, and alcohol. Calorically sweetened beverages (soda and fruit drinks) and caloric beverage with nutrients (fruit and vegetable juice, alcohol, and whole-fat milk) were each consumed by 60% of adults, whereas noncalorically sweetened beverages (diet beverages) were consumed by less than one-fifth of adults.

    Food and beverage clusters. Our final cluster solution resulted in 6 robust beverage patterns, which were observed across multiple iterations of analyses. The final beverage clusters include: Water and Tea; Coffee, Tea, and Water; Coffee and Soda; Coffee and Soda; Diet; Nutrients and Soda; and Soda (Table 2). Of particular interest is the clear differentiation between consumers of calorically sweetened and noncalorically sweetened beverages; there were no clusters in which both of these beverages are the predominant beverages consumed.

View larger version (10K): [in this window] [in a new window]   Figure 1  Odds ratio of being in a given beverage cluster based on being in a given food cluster. Results are from multinomial logistic models controlling for age, race, gender, income, and overweight status. Results are weighted to be nationally representative. Values represent OR ± SE, n = 9491. Beverage cluster abbreviations: W&T, Water and Tea; C&S, Coffee and Soda; N&S, Nutrients and Soda; S, Soda. *Differences within food cluster, compared with Coffee, Tea, and Water Cluster; P < 0.001.

View larger version (15K): [in this window] [in a new window]   Figure 2  Being in the Fast Food n = 1014 (A), Snacks and High-Fat Foods n = 1299 (B), Vegetables n = 418 (C), and Fruit and Low-Fat Dairy n = 415 (D) food clusters compared with not being in those clusters is linked with differential probability of consuming noncalorically sweetened beverages. Predicted probabilities are obtained from multinomial logistic models controlling for age, race, gender, income, education, and overweight status. Results are weighted to be nationally representative.

	Discussion

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
The overall beverage pattern of American adults is dominated by high energy intake from calorically sweetened beverages such as soft drinks, fruit drinks, alcohol, and high-fat milk, resulting in a total intake of 1.82 MJ/d (roughly 21% of energy) from beverages with little to no nutritional value. For example, although 20 and 26% of the population report consuming low-fat milk and fruit juice, respectively, these 2 beverages each account for roughly 350 mL of intake, whereas soft drinks provide 812 mL for the 46% of persons who are consumers. Most experts have explored the role of soft drinks and fruit drinks, the major calorically sweetened beverages, but have ignored other critical elements of the overall beverage consumption pattern. Moreover, there has been a lack of understanding of how beverages are associated with each other and who is consuming them. This has led to vague recommendations to increase intake of water, tea, coffee, and even noncalorically sweetened beverages. In this paper, we examine overall beverage patterns and examine the correlation of these patterns with diet.

We identified 6 mutually exclusive groups of beverage consumers whose overall patterns of beverage intake were distinctly different from one another. Despite many distinctions between these patterns, there was 1 key element that did not differentiate the clusters. Alcohol, representing roughly 10% of adult energy intake from beverages, was evenly distributed across the clusters, whereas other key beverages, including water, tea, coffee, and diet soft drinks, clustered independently of one another.

Persons in the 3 beverage patterns dominated by noncalorically sweetened beverages (Coffee, Tea, and Water; Water and Tea; and Diet) consumed less energy from beverages than persons who consumed 1 of the beverage patterns dominated by calorically sweetened beverages (Soda, Soda and Coffee, and Soda and Nutrients), which is not surprising given recent literature on the topic (3–5,28). These results provide insights into possible steps adults might take to reduce overall energy intake by reducing the amount of energy consumed from beverages.

The food clusters examined in this article are comparable to those reported elsewhere (10,29,30). Although studies of dietary intake using cluster analyses can be difficult to compare directly due do variations in cluster names, number of derived food clusters, and food group inclusions, there are many similarities. Regardless of the number of clusters reported, at least 1 cluster tends to define healthier eating patterns (characterized by greater consumption of vegetables, low-fat dairy products, and/or whole grains) and 1 defines less healthy eating patterns (characterized by greater consumption of desserts, high-fat foods, or fast food/convenience items). Newby et al. termed these "Healthy Pattern" and "Sweets Pattern," respectively (31), whereas Quatromoni et al. assigned names based on dietary quality, calling them "Heart Healthy" and "Empty Calorie" (32). Our food groups were named according to the foods that contributed the greatest amount to total intake as well as serving as unique identifiers between clusters. The parallels observed between our clusters and those reported in other studies give credence to our cluster groupings and may be suggestive of a general consistency in dietary patterns across of range of populations and study years.

We recognize that it is unrealistic to assume that food and beverage decisions are made independently. Therefore, we examined the associations between beverage and food intake by running multinomial logistic models of beverage cluster on food cluster. In general, we report that being in an unhealthy food cluster increased the probability that you were also in an unhealthy beverage cluster, and vice versa. For example, persons who were in the Fast Food cluster were more likely to be in the Soda Cluster, whereas persons in the Vegetable food cluster were less likely to be in this same beverage group. Furthermore, persons who were in one of the more unhealthy food clusters also had a smaller predicted probability of being in one of the healthier (noncaloric) beverage clusters compared with persons who were not in the unhealthy food clusters.

Consumption of individual beverages varies according to food cluster, even when persons in different food clusters are in the same beverage cluster. For example, compared with persons not in these clusters, those who were in the Vegetable and Fast Food clusters had an increased probability of being in the Nutrient and Soda group. This is contrary to what would be expected given that healthier food clusters are associated with healthier beverage clusters. A closer look at the actual consumption of the beverages in the Nutrient and Soda cluster, however, shows that whereas persons in the Vegetable and Fast Food clusters consumed nearly equal amounts of fruit and vegetable juice (nutrients: 502.8 mL), those in the Vegetable cluster consumed an average of 561.9 mL of soda, whereas those in the Fast Food cluster consumed an average of 798.5 mL of soda. Thus, people in a given beverage cluster consumed a different percentage of beverages within that cluster depending on food cluster.

We add to the current body of literature of dietary patterns by demonstrating that overall dietary patterns exist, even when consumption of foods and beverages are considered independent of one another. When food intake was used as a predictor of beverage intake, the patterns were similar to those found when food and beverage variables are clustered together.

There are several strengths and limitations with this study. Dietary intake was assessed using a single 24-h recall, which is not representative of usual intake at the level of the individual. However, our results were weighted to be nationally representative, which allows us to more readily draw conclusions about patterns in the general population. Further, we use a recognized method for elucidating patterns based on characteristics described by the data, rather than defining intake based on a priori patterns of dietary intake. Cluster analysis has been criticized for being too subjective, which we minimized by testing a range of final clusters (from 3 to 8 groups), specifying initial cluster centers that maximized the inter-to-intra cluster variability ratio before running the cluster program, and using predefined guidelines for selecting and naming final cluster solutions. Specifying initial cluster centers and using predefined guideline for selecting and naming clusters differentiates our methods from other cluster analyses of dietary patterns.

The Beverage Guidance Panel, the Institute of Medicine, and the USDA Dietary Guidelines groups all note the need to reduce intake of refined carbohydrates, particularly caloric sweeteners (33–36). This analysis has demonstrated that particular populations tend to be consumers of calorically sweetened beverages, and that these populations differ from those who are consuming noncaloric and diet beverages. Further, consumption of sweetened beverages seems to be closely linked to less healthy eating patterns, such as those defined by high-fat foods, fast foods, and snacks. This is disconcerting because both calorically sweetened beverages and fast food/high-fat foods have been shown to be associated with overweight and long-term weight gain, thus increasing the risk of adverse health outcomes among these particular populations.

There are important implications of these findings. First, increasing awareness of the role that beverages play in overall energy intake may help promote the substitution of noncaloric beverage for calorically sweetened ones. Second, this work has identified a clear target for intervention messages: consumers of less healthy foods. Targeting policies or programs at this audience may be the best method for decreasing energy intake from beverages and have the greatest impact on behavior change, especially leading to reduced energy intake, because food and beverage consumption appear to be intimately linked.

	ACKNOWLEDGMENTS

 
We thank Ms. Frances L. Dancy for administrative assistance, Mr. Tom Swasey for graphics support, and Mr. Dan Blanchette for programming assistance.

	FOOTNOTES

 
¹ Supported by NIH (R01-CA109831, R01-CA121152) and Unilever Health Institute.

² Supplemental Table 1 is available with the online posting of this paper at jn.nutrition.org.

Manuscript received 1 May 2006. Initial review completed 3 June 2006. Revision accepted 14 August 2006.

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TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 

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