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Dietary patterns and socioeconomic position

Abstract

Background/Objectives:

To test a socioeconomic hypothesis on three dietary patterns and to describe the relation between three commonly used methods to determine dietary patterns, namely Healthy Eating Index, Mediterranean Diet Score and principal component analysis.

Subjects/Methods:

Cross-sectional design in 1852 military men. Using mailed questionnaires, the food consumption frequency was recorded.

Results:

The correlation coefficients between the three dietary patterns varied between 0.43 and 0.62. The highest correlation was found between Healthy Eating Index and Healthy Dietary Pattern (principal components analysis). Cohen's kappa coefficient of agreement varied between 0.10 and 0.20. After age-adjustment, education and income remained associated with the most healthy dietary pattern. Even when both socioeconomic indicators were used together in one model, higher income and education were associated with higher scores for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern. The least healthy quintiles of dietary pattern as measured by the three methods were associated with a clustering of unhealthy behaviors, that is, smoking, low physical activity, highest intake of total fat and saturated fatty acids, and low intakes of fruits and vegetables.

Conclusions:

The three dietary patterns used indicated that the most healthy patterns were associated with a higher socioeconomic position, while lower patterns were associated with several unhealthy behaviors.

Introduction

Dietary pattern analysis, based on the concept that foods eaten together are as important as a reductive methodology characterized by a single food or nutrient analysis, has emerged more than a decade ago as an alternative approach to study the relation between nutrition and disease (Schwerin et al., 1982; Randall et al., 1991). As reviewed by Hu (Hu, 2002), dietary pattern analysis is a better method to examine the effect of overall diet: food and nutrients are not eaten in isolation, and the ‘single food or nutrient’ approach will not take into account the complex interactions between food and nutrients. Two major methods are used to reduce complex dietary data: a hypothesis-oriented approach using previous information to stratify a dietary pattern and a statistical approach using study-specific data to rank individuals, that is, principal component analysis or reduced rank regression models (Hoffmann et al., 2004; Schulze and Hoffmann, 2006).

The Healthy Eating Index and the Mediterranean Diet Score are two frequently used hypothesis-oriented approaches (Waijers et al., 2007; Arvaniti and Panagiotakos, 2008; Fransen and Ocke, 2008; Kourlaba and Panagiotakos, 2009). The Healthy Eating Index represents the degree to which a dietary pattern conforms to official guidelines summarized in the United States Department of Agriculture Food Guide Pyramid (Kennedy et al., 1995). The Mediterranean Diet Score, according with the Mediterranean dietary pattern, has received a lot of attention because of the associated reduction in mortality (Sofi et al., 2008).

An example of commonly used exploratory approach is principal component analysis identifying foods that are consumed together. This statistical technique may be able to detect correlations between foods or food groups contained in an array of nutritional data.

Few publications have reviewed the different methods to determine a dietary pattern (Hu, 2002; Kourlaba and Panagiotakos, 2009). A first aim of this study was to compare the degree of accordance of Healthy Eating Index, Mediterranean Diet Score and principal component analysis on the ranking of individuals according to their dietary patterns.

The second aim of this study was to describe the relation between Healthy Eating Index, Mediterranean Diet Score and principal component analysis versus education and income as indicators for socioeconomic position. A healthy dietary pattern has been consistently associated with a higher socioeconomic position (Dynesen et al., 2003; Huot et al., 2004; Robinson et al., 2004; Park et al., 2005; Kant and Graubard, 2007).

To our knowledge, the relation between socioeconomic position and different methods to describe a dietary pattern has seldom been studied.

Materials and methods

In February 2007, air and terrestrial components of the Belgian army totaled 33 053 men. After stratification for military rank and age, 5000 men were selected representative for the total army structure. The selection consisted of 598 officers; 2103 non-commissioned officers and 2299 soldiers. This population has the advantage to limit the influence of occupation as socioeconomic determinants allowing us to focus on the influence of income and education.

A semi-quantitative food frequency questionnaire with 150 food items was mailed to the participants. The following categories of consumption frequency were used: never, one to three times a month, once a week, two to four times a week, five to six times a week, once a day, two to three times a day, four to six times a day and more than six times a day. Portion sizes were predefined using familiar measuring devices (teaspoon, glass, cup...). The validity of the questionnaire was tested on a sample of 100 men representative for the participants to the cross-sectional study (Mullie et al., 2009).

A second questionnaire was used to register health-related and lifestyle characteristics. This questionnaire was self-reporting regarding smoking, marital status, main occupation, age, weight, height, number of children and knowledge of cardiovascular risk factors. This questionnaire was used in previous research (Autier et al., 2003). Yearly gross salary was obtained from administrative services, taking into account the rank and years of active duty.

The individual characteristics of the responders were categorized in age-category (20 to 29 years, 30 to 39 years, 40 to 49 years and 50 to 59 years); Body mass index (BMI) classified according to the World Health Organization in normal weight, 18.5BMI <25.0 kg/m2, overweight, 25.0BMI <30.0 kg/m2 and obesity, BMI30.0 kg/m2 (World Health Organisation, 2003); physical activity (stratified in low, moderate and high according to the International Physical Activity Questionnaire) (Hallal and Victora, 2004); use of vitamin supplements (yes or no); actual smoking (yes or no); educational level (low for vocational education, moderate for secondary level and high for bachelor or master level); and income (low for lowest tertile of yearly gross income of the group, moderate for intermediate tertile of income and high for highest tertile of yearly income).

Participation was on a voluntary basis and without incentives. An informed consent was signed by all participants.

Statistical analysis

Using a χ2 test, we assessed the differences in the proportion of officers, non-commissioned officers and soldiers that responded. Using data from military records, that is, age and rank, the differences between responders and non-responders were tested with the same test. For descriptive statistics, mean and s.d. were calculated for the individual characteristics, according to quintiles of dietary patterns. Differences between quintiles were tested with χ2 and analysis of variance.

The Healthy Eating Index and the Mediterranean Diet Score were computed as described earlier (Basiotis et al., 2002; Sofi et al., 2008). The possible scores for Healthy Eating Index ranged from 0 to 100 and for Mediterranean Diet Score from 0 to 9, with a high score for the most healthy pattern. Principal component analysis was applied to the data of the semi-quantitative food frequency questionnaire. First, 150 food items were classified into 34 predefined food groups with similar nutrient profile, according to Hu et al. (2000). Principal components analysis was used to derive dietary patterns based on the 34 food groups. Varimax transformation was effectuated to achieve uncorrelated factors with a greater interpretability. Components with eigenvalues more than 1.5, interpretability of the factors and Scree plot were used to determine the number of selected factors. The eigenvalues of the factors dropped after the second factor (from 2.44 to 1.77) and after the third factor (from 1.77 to 1.44). The remaining factors were more similar after the fourth factor (ranging from 1.38 for the fifth factor to 1.10 for the tenth factor). Three major dietary patterns were clearly identified for further analysis. The factor scores for each pattern were constructed by summing up the observed intakes of the component food items, weighted by the individual factor loadings. Those factor scores rank individuals according to their agreement with each dietary pattern. The most healthy dietary pattern was selected, that is, the Healthy Dietary Pattern (principal components analysis), because a high factor score is associated with the most healthy pattern, which is also the case for Healthy Eating Index and Mediterranean Diet Score. This Healthy Dietary Pattern was associated with a high intake of fruits, vegetables, nuts, fish, whole grain and low-fat diary products. Participants were divided in quintiles according to the scores for Healthy Eating Index, Mediterranean Diet Score and factor scores for the Healthy Dietary Pattern (principal components analysis). Spearman correlation coefficients, percentages of participants classified into the same and opposite quintiles of intake, and Cohen's kappa coefficient of agreement were calculated for the three dietary patterns.

Age-adjusted and BMI stratified linear regression was executed to separately estimate the independent effect of education and income categories on Healthy Eating Index, Mediterranean Diet Score and principal components analysis dietary pattern as continuous dependent variable. Tolerances were checked for all variables. Plots of the residuals versus the predicted values were examined to ascertain that basic model assumptions were met. Correlation between education and income was 0.4, which excluded multicollinearity problems. A two-sided level 0.05 level of significance was defined. SPSS 16.0 (SPSS Inc., Chicago, IL, USA) statistics software was used. The Bioethical Committee of the University of Leuven approved the complete research protocol.

Results

Table 1 presents the demographic and lifestyle characteristics of the participants. Out of the 5000 selected men, only 37% participated to the study. The most prevalent age-category was 40–49 years, 76% were non-smokers. Approximately 58% had a BMI25.0 kg/m2 while 42.6% had a low level of education. Responders to the mailing tended to be older than non-responders (74.3% were older than 40 years compared with 61.4% for the non-responders) (P<0.001). Moreover, soldiers were less incline to participate to the study than officers and non-commissioned officers (P<0.001).

Table 1 Characteristics of the participants

Tables 2 and 3 present the factor groupings used in the principal component analysis and the factor-loading matrix for the three major factors identified by using food consumption data from the food frequency questionnaire. The greater the factor loading for a specific food or food item, the greater the effect of that food or food item to a specific factor. The first factor was heavily loaded with red meats, processed meats, beer, garlic, tomatoes, wine, eggs, poultry, liquor, organ meats and vegetables. This factor explained 7.4% of the total variance. This was labelled Meat Dietary Pattern. The second factor, explaining 7.2% of the total variance, was more loaded for tomatoes, fruit, low-fat diary products, whole grain, vegetables, cold breakfast cereals, fruit juice, fish, tea and nuts. This was labelled Healthy Dietary Pattern. The last factor, explaining 6.2% of the total variance, was heavily loaded with red meats, processed meats, sweets, desserts, snacks, high-energy drinks, high-fat diary products, refined grains, mayonnaise and potatoes. This was labelled Sweet Dietary Pattern.

Table 2 Factor groupings used in the dietary pattern analysis
Table 3 Factor-loading matrix for the major factors identified by using food consumption data from the food frequency questionnairea

Table 4 presents the distribution of lifestyle and nutritional exposure in function of the quintiles of Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern (principal components analysis). The range of the scores was for the lowest and the highest quintiles of Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern, respectively 22–45 and 68–95; 0–2 and 6–9; −3.6 to −0.8 and 0.7–4.8. There was no relation between the quintiles of dietary patterns and age or BMI. The highest quintiles of Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern were systematically associated with higher physical activity (all P<0.001), general use of vitamin supplements (all P<0.001), non-smoking (all P<0.001), high education (all P<0.05), high income (P<0.001, except for the Healthy Eating Index). The highest quintiles of the three dietary patterns were associated with the lowest daily intake of total fat (P<0.001), saturated fatty acids (P<0.001), mono-unsaturated fatty acids (P<0.001), poly-unsaturated fatty acids (P<0.001 except for the Mediterranean Diet Score) and with the highest intake of carbohydrates (P<0.001), all expressed in energy-percent.

Table 4 Baseline characteristics according to Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern (principal components analysis) (n=1852)a

In Table 5, the Spearman correlation coefficients, percentages of participants classified into the same and opposite quintiles of intake, and Cohen's kappa coefficient are presented. The highest correlation was found between Healthy Eating Index and Healthy Dietary Pattern, the lowest between Healthy Eating Index and Mediterranean Diet Score. Surprisingly, the kappa coefficients expressed only slight agreements between the three dietary patterns.

Table 5 Correlation coefficients, percentages of participants classified into the same and opposite quintiles of intake, and Cohen's kappa coefficient (K) for Healthy Eating Indexb, Mediterranean Diet Scoreb and Healthy Dietary Pattern (principal components analysis) (n=1852)b

Table 6 expresses the relation between the socioeconomic indicators education and income and the three dietary patterns, stratified in normal weight, overweight and obesity. All the age-adjusted linear regressions showed the same relation, that is, a higher education or income level was associated with the most healthy dietary pattern, independently of the weigth-category. When the models were adjusted for age and for both indicators, the socioeconomic relation is attenuated but still present. In summary, a higher socioeconomic position was associated with an increasing score for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern: in the category normal weight, the score increased respectively with 1.59, 0.25 and 0.10 with increasing education; and with 1.38, 0.28 and 0.11 for increasing income (Table 6).

Table 6 Age-adjusted linear regression with dietary pattern as continuous dependent variable: effects of educational categories and income categories unadjusted for each other (Model 1), and simultaneous adjustment for both socioeconomic indicators (Model 2) (n=1852)

Discussion

The first aim of this work was to study the relation between three commonly used methods to determine dietary patterns, namely Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern (principal components analysis). The low correlation and Cohen's kappa coefficient of agreement did not influence the hypothesis, that is, the relation between socioeconomic indicators and dietary patterns.

After age-adjustment, education and income remained associated with the most healthy dietary pattern. Even when both socioeconomic indicators were used together in a model, a higher income and education were associated with a higher score for Healthy Eating Index, Mediterranean Diet Score and Healthy Dietary Pattern. Stratification in normal weight, overweight and obesity did not influence the relation between socioeconomic indicator and dietary pattern. Although socioeconomic differences in prevalence of obesity have been described, that is, higher prevalence for the lowest socioeconomic positions (Mullie et al., 2008), dietary pattern analysis seems not to be able to detect a specific dietary pattern explaining this socioeconomic occurrence of obesity.

The positive association between socioeconomic position and dietary pattern has been confirmed by research carried out on different populations, using different indexes or statistical techniques to determine dietary patterns. As reviewed by Darmon et al. (2008), higher values of Healthy Eating Index (Loughley et al., 2004; Angelopoulos et al., 2009; Manios et al., 2009), Diet Quality Index (Patterson et al., 1994; Lallukka et al., 2006), dietary diversity scores (Kant and Graubard, 2007), and other diet-quality measures (Groth et al., 2001; Dynesen et al., 2003; Robinson et al., 2004) have all been associated with a higher socioeconomic position, usually estimated by education level. Using principal components analysis to determine dietary patterns, Robinson et al. (2004) found that educational attainment was the most important determinant of a healthy eating pattern. The general observed socioeconomic nutritional gradient can be mediated by food costs, meaning that lowest cost diets mainly consumed by the lowest socioeconomic positions are generally unhealthy. People who have less money, choose to buy cheaper foods, and these cheaper foods are less healthy (Drewnowski, 2003; Drewnowski and Darmon, 2005; Darmon and Drewnowski, 2008). In this study, the least healthy quintiles of dietary pattern measured by the three methods were associated with a clustering of unhealthy behaviors, that is, smoking, low physical activity, highest intake of total fat and saturated fatty acids, and low intakes of fruits and vegetables.

The correlation between Healthy Eating Index and Mediterranean Diet Score was 0.43, a rather moderate correlation coefficient. This may be explained by the fact that the two hypothesis-based indexes have different foundations: the United States Department of Agriculture Food Guide Pyramid for the Healthy Eating Index and the Mediterranean dietary pattern for the second. Both indexes had a rather comparable score system for vegetables, fruits, milk and meat but the other components will differ. The Mediterranean Diet Score focused on the ratio monounsaturated fatty acids on saturated fatty acids, legumes and alcohol; the Healthy Eating index on total fat, saturated fat, cholesterol, sodium and diet variety. A major drawback of the Healthy Eating Index is that it is unable to distinguish between whole grains and refined grains, which will limit the capacity to assess dietary fibers (Arvaniti and Panagiotakos, 2008). Second, in the Mediterranean Diet Score the median intake of each component serves as cut-off value. This approach does not automatically means that a high score is associated with a healthy level of intake. The consequence of the binary method used to categorize intakes in two groups is that people with moderate high or low intake of a component are classified in the same category as people with a very high or very low intake. The advantage of using the median is that half of the participants will score positively and half negatively, creating enough contrast for further research (Waijers et al., 2007). The Healthy Eating Index is a scoring system based on current views of healthy eating, which allows more comparisons between populations because the scoring system remains the same. A major disadvantage of the Healthy Eating Index is the low discriminative power of the components if all the participants have a low score for a component. In Western diets with a high intake of saturated fatty acids, the discriminative power of those fatty acids could be very low. Moreover, the fact that energy intake may be a confounder, that is, participants with a high intake will more easily meet the guidelines, can not be excluded for the Mediterranean Diet Score. The scoring system of the Healthy Eating Index depends on the recommended energy intakes: the adequate number of servings is expressed according to energy intake level, based on sex and age.

The high correlation coefficient between Healthy Eating Index and Healthy Dietary Pattern (principal components analysis) can be the consequence of overlapping components: high scores for Healthy Eating Index and for Healthy Dietary Pattern (principal components analysis) are characterized by high intake of fruits, vegetables, cereals and low intake of meat and diary products, with low intake of total fat, cholesterol and saturated fatty acids as a consequence.

The use of principal component analysis involves that several arbitrary decisions must be taken, such as the number of retained factors and the labels of the factors. The value of the labelling can be judged from the presented factor loadings. Moreover, the percentage of the variance explained by the factors in this study (20.8%) is comparable to other studies using comparable statistical methods (Hu et al., 2000; van Dam et al., 2003; Slattery, 2008). Three distinct dietary patterns were identified; similar factor loadings were extracted in other studies when two or three major patterns were selected (Hu et al., 2000; Schulze et al., 2001; Kim et al., 2004; Park et al., 2005).

Some limitations of this study are worth noting. The response in this study was only 37%, but information could be gathered regarding non-responders. The responders were older than non-responders. A military population was selected for this study. This population has the advantage of limiting the influence of occupation as socioeconomic determinant, which allowed us to restrain our investigations to the influence of income and education as socioeconomic indicators. A second advantage is that we could have exact figures regarding income from administrative services. The sample can be observed as representative for Belgian army men. However, because of the different manual and non-manual tasks, occupations and education levels present in an army, our sample can be observed as a representative sample for men with an occupation. Moreover, our nutritional and lifestyle results match with the results of a recent cross-sectional study on a representative Belgian male population (Devriese et al., 2006).

The low correlation between education and income in this study (r=0.40) indicate that each indicator involve different components of exposition variability. The correlation was comparable to other publications (Liberatos et al., 1988; Winkleby et al., 1992; Galobardes et al., 2001; Braveman et al., 2005). Colinearity and unstable models did not influence, because the correlation between education and income is below 0.50 (Winkleby et al., 1992; Turrell et al., 2003; Braveman et al., 2005).

In conclusion, in this study, a higher socioeconomic position as measured by education and income was systematically associated with a more healthy dietary pattern, independently of the method to determine the dietary pattern. Healthy Eating Index, Mediterranean Diet score and Healthy Dietary Pattern (principal components analysis) obtained a comparable ranking. From a practical point of view, the choice of the Healthy Eating Index or the Mediterranean Diet Score seems to be obvious, because those two methods are less time consuming than principal component analysis. Moreover, principal component analysis involves that several arbitrary decisions must be taken.

Conflict of interest

The authors declare no conflict of interest.

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Acknowledgements

The authors are indebted to the participants of this study. The authors thank Ms Jeanine De Leeuw for her valuable assistance in realizing this study.

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Correspondence to P Mullie.

Additional information

Contributors: PM conceived the original idea together with GV, performed the study, analyzed the data and wrote the first draft; PC and MH conceived and refined the original idea, advised on study design and data analysis and critically appraised the paper.

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Mullie, P., Clarys, P., Hulens, M. et al. Dietary patterns and socioeconomic position. Eur J Clin Nutr 64, 231–238 (2010). https://doi.org/10.1038/ejcn.2009.145

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Keywords

  • dietary pattern
  • nutritional assessment
  • nutritional epidemiology
  • public health
  • socioeconomic status
  • prevention

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