Nut Consumption Is Associated with a Healthy Dietary Pattern in Military Men ()
1. Introduction
Nut consumption has been related to a reduced risk of coronary heart disease, probably by lowering blood lipids levels. Nuts are rich in unsaturated fatty acids, dietary fibers, and phytosterols. Additionally, nuts are rich in copper, magnesium, potassium, folic acid, niacin, vitamin E and vitamin B6. As recently reviewed by Sabaté et al. [1], and based only on human intervention studies, the effects of nuts on blood lipids were dose related and the lipid-lowering effects were greatest among subjects with high baseline LDL-cholesterol. A daily nut consumption of 67 g was associated with a mean reduction of total cholesterol by 5.1%. A major problem of nut consumption is the high fat content and thus the high energy-density. The total fat content of nuts varies between 45% to 75%. Stimulating regular consumption of nuts can be in conflict with the actual obesity epidemic, where prevention and treatment is associated with more energy-restrictive and low energy-density nutritional patterns [2].
An interesting finding in the review of Sabaté et al. [1] is the nut diet by Body Mass Index (BMI) interaction: the cholesterol-lowering effect of nut consumption is less pronounced in subjects with obesity. This rather puzzling consideration about nut consumption emphasizes on one hand the fact that obesity may be associated with elevated endogenous production of cholesterol. On the other hand, an association between nuts consumption and healthy dietary pattern could partly play a role in explaining the reduction of coronary heart disease reported in observational epidemiologic research [1].
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 [3]. As reviewed by Hu [4], 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 (principal component analysis or reduced rank regression models) [5]. The Healthy Eating Index and the Mediterranean Diet Score are two frequently used hypothesis-oriented approaches. 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 [6]{Please_Select_Citation_From_Mendeley_Desktop}. The Mediterranean Diet Score, according with the Mediterranean dietary pattern, has received a lot of attention because of the associated reduction in mortality [7]. 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.
The aim of this study was to describe the relation between nut consumption and general dietary patterns as described by Healthy Eating Index, Mediterranean Diet Score and principal component analysis.
2. 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. 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 [8]. 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 [9]. 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) and Body mass index (BMI) classified in normal weight, 18.5 ≤ BMI > 25.0 kg/m2, overweight, 25.0 ≤ BMI < 30.0 kg/m2 and obesity, BMI > 30.0 kg/m2. Participation was on a voluntary basis and without incentives. An informed consent was signed by all participants.
The nut consumption was dichotomized in one or more than one portion a week versus less than one portion a week. The family of popular nuts includes almonds, cashews, hazelnuts, pecans, pine nuts, pistachios, and walnuts. The questionnaire focused exclusively on the total nut consumption as whole, not on nuts consumed in hidden sources in recipes or on nuts consumed from spreads. Consumption of hazelnut spread was not seen as a source of nuts, because health-related effects after hazelnut spread were not recognized in the literature.
Using a χ2 test, we assessed the differences in the proportion of responding 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 standard deviations were calculated for the individual characteristics, according to quintiles of dietary patterns. Differences between quintiles were tested with χ2 and analysis of variance. Daily intake of nuts, fish, red meat, processed meat, legumes, fruits and vegetables were calculated according to quintiles of dietary patterns. The distributions of foods did not follow a normal distribution, we first used non-parametrical tests as indicated. However, the results of the non-parametrical tests were similar to the parametrical tests, we choose the latest for a clear presentation of data. The Healthy Eating Index and the Mediterranean Diet Score were computed as described earlier [6,7,10]. 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 healthiest 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. [11]. 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 healthiest dietary pattern was selected, that is, the Healthy Dietary Pattern (principal components analysis), because a high factor score is associated with the healthiest 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 dairy products.
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.
3. Results
Table 1 presents the characteristics of the subjects. Out of the 5000 selected men, only 37% participated to the study. The most prevalent age-category was 40 to 49 years. About 58% had a BMI ≥ 25.0 kg/m2. Responders to the mailing tended to be older than non-responders (74.3% were older than 40 years compared to 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). Of all participants, 14% consumed one or more portion of nuts a week. The mean (SD) daily portion of nuts consumed was 5.0 gram (±0.9).
Table 2 presents 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 impact 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 dairy 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 dairy products, refined grains, mayonnaise and potatoes. This was labelled Sweet Dietary Pattern.