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Department of Nutrition, School of Public Health and Food Security and Nutrition Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
* To whom correspondence should be addressed. E-mail: esmaillzadeh{at}hlth.mui.ac.ir.
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ABSTRACT |
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 TOP ABSTRACT IntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Our understanding of how and why obesity develops is incomplete but involves the integration of social, behavioral, cultural, physiological, metabolic, and genetic factors (1). Rising prevalence of obesity reflects the strong impact of lifestyle factors, including diet, on its etiology (9). It has recently been recommended to consider diet as dietary patterns to capture a snapshot of its entirety (10,11). Due to the possibility of many undiscovered compounds in foods, the enormity of interactions among nutrients and foods, and the colinearity among food and nutrient intakes, using a multivariate approach like dietary patterns could resolve concerns about confounding factors and interactions of foods and nutrients (10–13). Furthermore, a dietary pattern approach reflects individuals' dietary behaviors and therefore could provide more detailed information about nutritional etiology of chronic disease (11,12).
Several studies have reported the association of major dietary patterns with obesity and central adiposity (14–21); most came from western countries (14–19) and few data are available from non-western ones (20,21), particularly from Middle-Eastern countries, where we are aware of no study to report such an association. Studying the links between dietary patterns and different forms of obesity is especially relevant for Middle-Eastern populations, because of their high prevalence of a particular type of obesity, the so-called Middle-Eastern pattern, which makes them very susceptible to increased risk of obesity-related comorbidities. The predominant characteristic of this pattern of obesity is central fat accumulation and enlarged waist circumference (WC),4 particularly among women; >50% of adult women in these countries are abdominally obese (22). Unlike most western populations, the prevalence of obesity and central adiposity among Middle-Eastern women is higher than that among men (22). Besides different patterns of obesity, dietary intake of the Middle-Eastern population has its own unique characteristics: large portion sizes with high intake of refined grains (white rice and bread) and hydrogenated fats and a greater percentage of energy from carbohydrates. With these features, factor analysis might give different dietary patterns in this region compared with those from other parts of the world. In this study, we examined if major dietary patterns are related to the prevalence of general obesity and central adiposity among Iranian women.
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Subjects and Methods |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Assessment of dietary intake. We used a validated FFQ for assessing usual dietary intakes (23,24). The FFQ was a semiquantitative Willett-format questionnaire with 168 food items listed. A trained dietitian administered all the questionnaires. Participants reported their frequency of consumption of a given serving of each food item during the previous year on a daily (e.g. bread), weekly (e.g. rice, meat), or monthly (e.g. fish) basis. For our analysis, daily intake of all food items from FFQ was computed and then consumed foods were converted to grams using household measures (25). To identify dietary patterns, first we categorized 168 food items into 41 predefined food groups based on the similarity of nutrients (Supplemental Table 1). In some cases, we defined food groups as an individual food because of their unique nutrient profiles (e.g. eggs, margarine, coffee, and tea).
Assessment of anthropometric measures.
Detailed information regarding measurement of weight, height, and WC has been given elsewhere (26). Briefly, weight was measured to the nearest 100 g without shoes while wearing minimal clothes. Height was measured without shoes with shoulders in a normal position. BMI was calculated as weight in kilograms divided by height in meters squared. In the current study, general obesity was defined as BMI 
30 kg/m2. WC was measured at the narrowest level and that of the hip at the maximum level over light clothing using an unstretched tape measure, without any pressure to body surface; measurements were recorded to the nearest 0.1 cm. To reduce error, all measurements were taken by the same technician. We defined abdominal adiposity as WC 88 cm.
Assessment of other variables. As reported previously (27), data on physical activity were obtained using an interview-based questionnaire and expressed as metabolic equivalent h/wk (MET-h/wk) (28). Additional covariate information regarding age, smoking habits, socioeconomic status, medical history, and current use of medications was obtained using questionnaires.
Statistical methods. We used principal component analysis with orthogonal transformation to identify major dietary patterns. Factors retained for further analysis were based on their natural interpretation, Eigenvalues (>1), and Scree test (29). The derived factors were labeled on the basis of our interpretation of the data. We computed the factor score for each pattern by summing intakes of food groups weighted by their factor loadings (29). Each participant received a factor score for each identified pattern.
Subjects were categorized based on quintiles of dietary pattern scores. To compare general characteristics across quintiles, we used 1-way ANOVA and chi-square tests where appropriate. Dietary intakes (age and energy adjusted) were compared by using ANCOVA. Multivariate adjusted means for anthropometric measures were computed using general linear model in different models controlling for age (y), smoking (yes or no), current estrogen use (yes or no), and socioeconomic status (categorical) in model I, additionally for physical activity (MET-h/wk) in model II and further for energy intake (kcal/d)5 in model III. Multivariable logistic regression models were used to obtain adjusted OR. Covariates were the same as above. The Mantel-Haenszel extension chi-square test was used to assess the overall trend of OR across increasing quintiles of dietary patterns scores. P < 0.05 was considered significant. Statistics presented in the text are OR and 95% CI. SPSS (version 9.05) was used for all statistical analyses.
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Results |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Individuals in the upper quintile of healthy dietary pattern score were more physically active and less likely to be generally and centrally obese compared with those in the lowest quintile (Table 1). Subjects in the top quintile of the western dietary pattern score were less likely to exercise and had higher prevalence of general and central obesity. Compared with those in the lowest quintile, individuals in the upper quintile of Iranian dietary pattern were older, slightly more physically active, less likely to be generally obese, and more likely to be centrally obese. Lower intakes of energy and cholesterol and higher intakes of fiber were seen among those in top quintile of healthy dietary pattern. In contrast, participants in the top quintile of the western dietary pattern consumed more energy and cholesterol and less fiber. Being in the upper quintile of Iranian dietary pattern was associated with slightly lower energy intake.
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Discussion |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Studies that have identified dietary patterns in developing countries are scarce. The patterns extracted in our study were similar to those found in earlier studies on adult populations. In the Health Professionals' Follow-up Study, Hu et al. (30) identified 2 major dietary patterns named "prudent" (including vegetables, fruits, legumes, whole grains, and fish) and "western" (including processed meat, red meat, butter, high-fat dairy products, eggs, and refined grain). Similar dietary patterns were found in the Nurses Health Study (31) and other studies that included American women (32). Khani et al. (33), investigating the participants of the Swedish Mammography Cohort, reported 3 major dietary patterns labeled healthy (high in vegetables, fruits, fish, poultry, tomato, cereal, and low-fat dairy products), western (processed meat, meat, refined grains, sweets, and fried potatoes) and drinker (beer, wine, liquor, and snacks). The healthy and western patterns in this study are very similar to the prudent and western patterns reported by Hu et al. (30) and are comparable to the healthy and western patterns reported by Khani et al. (33). It is remarkable that no matter which population dietary patterns originated from, healthy and western patterns seem to emerge, as shown in many previous studies on dietary patterns.
Identifying the association between major dietary patterns and obesity is not new. However, it is always interesting to see what kinds of dietary patterns exist in different parts of the world and to what extent these patterns are related to the obesity epidemic. In this study, we found an inverse relationship between a healthy dietary pattern and risk of general and central adiposity. This is consistent with previously reported findings in American (34) and European (35) studies. Other studies found that a dietary pattern characterized by low-fat dairy, grains, and fruit was inversely associated with changes in BMI and WC in women (15,36). Inverse associations have also been reported between major dietary patterns characterized by whole grains, fruits, and vegetables with BMI and weight gain (37,38). However, some studies have reported no significant association (P = 0.49) between healthy dietary pattern and BMI (19). This might be attributed to the self-reported weight and height in these studies. Our western dietary pattern was positively associated with increased risk of general and central obesity. Both cross-sectional (39,40) and prospective studies (41,42) have shown similar findings previously. A "meats" dietary pattern, obtained by factor analysis, in a group of Hawaiian women was associated with higher BMI (39). A positive association between the western dietary pattern and obesity has also been reported by Slattery et al. (40). In an 8-y prospective study among >50,000 adult women in the Nurses' Health Study, Schulz et al. (41) reported that the adoption of a western dietary pattern is associated with greater weight gain. Higher intakes of meat and sweets, as seen in our western pattern, were associated with weight gain over a 2-y follow-up period among men and women in the European Prospective Investigation into Cancer and Nutrition-Potsdam Study (42). Overall, these findings underscore the importance of westernized diet and nutrition transition in the alarming prevalence of obesity in developing countries. The Iranian dietary pattern we defined in this study was not consistently associated with general or central obesity; however, subjects in the 3rd quintile had greater odds of being centrally obese. The complex nature of this dietary pattern makes interpretation very difficult. The Iranian diet, as is clear in our finding, is highly loaded with refined grains (white rice and bread), tea, potatoes, and hydrogenated fats. With these components, one would expect to find a positive association between this dietary pattern and risk of obesity. However, some healthy food groups like legumes and whole grains were also loaded in this dietary pattern that could interact with other foods in the pattern to counteract their effect on obesity.
Some points should be considered in interpreting our findings. First, due to the cross-sectional design of the study, one cannot infer causality. Therefore, our findings need to be confirmed in future prospective studies. Furthermore, it is possible that certain anthropometric patterns could have led to changes in diet in hope of changing these measures. Although these changes would confound the association between dietary patterns and obesity, such residual confounding effects would tend to attenuate the risk estimates. Therefore, the true results are even stronger than what we reached. Second, the possibility of residual confounding could not be excluded, because we did not consider participants' dietary behaviors in our dietary pattern analysis. Third, limitations of FFQ for assessing dietary intakes should be taken into account. Fourth, several subjective or arbitrary decisions in the use of factor analysis need to be considered (43). Fifth, we measured WC at the point of noticeable waist narrowing, which may have resulted in lower WC values than might be obtained using other common sites of measurement. This location of waist measuring might also have resulted in some measurement errors, because each person would have a different area of the abdomen that is the narrowest part. Such potential source of error is particularly important for this study that aims to evaluate the Middle-Eastern pattern of obesity whose main characteristic is abdominal obesity and enlarged WC. While the WHO Expert Committee (44) on Physical Status recommends measurement mid-way between the lower rib and the iliac crest, the NHANES III guidelines (45) prescribe use of a point just above the right ileum and the recommendation of the North American Association for the Study of Obesity and the National Heart, Lung and Blood Institute (46) is to use the right iliac crest. The lack of standard measurement for WC is unfortunate and makes comparison with other studies difficult. It is thought that the use of narrowest waist measurement offers greater ease of acceptance and interpretation by the public and may facilitate self-measurement in addition to clinical use.
In conclusion, our findings suggest that a dietary pattern characterized by high consumption of fruits, vegetables, poultry, and legumes is associated with lower risk of general and central obesity, while a dietary pattern with high amounts of refined grains, red meat, butter, processed meat, and high-fat dairy products and low amounts of vegetables and low-fat dairy products is associated with increased risk of these conditions. Future prospective studies are required to confirm these findings.
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FOOTNOTES |
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2 Author disclosures: A. Esmaillzadeh and L. Azadbakht, no conflicts of interest.
3 Supplemental Tables 1 and 2 are available with the online posting of this paper at jn.nutrition.org.
4 Met-h/wk, metabolic equivalent h/wk; WC, waist circumference; WHR, waist-to-hip ratio.
Manuscript received 1 October 2007. Initial review completed 31 October 2007. Revision accepted 14 November 2007.
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LITERATURE CITED |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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