Introduction

Obesity is a chronic, heterogeneous, multifactorial disease with an increase in fat/lean ratio, frequently accompanied by an increase in body weight, the magnitude and distribution of which conditions health [1]. Although the genetic component cannot be denied, it is unlikely to be the factor that explains its recent higher global prevalence, not only in developed but also in developing countries. There is a complex interaction between genes and environmental factors, such as dietary habits and physical activity, which influence its high prevalence. Examples of these are the great consumption of processed foods outside the home, high-energy foods, with a low concentration of micronutrients, rich in saturated fats as well as refined carbohydrates [2–6].

The phenomenon of dietary change worldwide has been called nutritional transition. According to a World Health Organization–Food and Agriculture Organization (WHO-FAO) report, changes in lifestyle and diet, together with sedentarism and less physical activity, have affected health and nutrition [5]. Food intake in general is related to habits, which are influenced by the family’s purchasing power. Socio-economic status (SES) as well as education level influence the selection and preferences in food according to the social and environmental situation.

Overweight and obesity are the fifth leading risk factor for death in the world [7]. It is estimated that 1.46 billion adults (1.41–1.51 billion) worldwide had a BMI of 25 kg/m2 or higher, of whom 205 million men (193–217 million) and 297 million women (280–315 million) were obese [8]. The prevalence of overweight/obesity in Córdoba, Argentina, has been recently published, including its association with some social and cultural factors, but a characterization of dietary habits is still lacking.

Obesity is also a public health issue in Córdoba Argentina [9]. Its population, which has a monotonous diet with an unusually high consumption of mainly barbecued red meat and a low fiber intake, as reported by Pou et al. [10, 11], constitutes an interesting scenario to perform studies in the field of the nutritional epidemiology of chronic diseases. The aim of this cross-sectional population-based random-sample study is to estimate an updated prevalence of overweight/obesity, to characterize Córdoba population’s diet, and to identify an association between diet and overweight/obesity. We hypothesized that the prevalence of overweight and obesity in the population of Córdoba is associated with a high intake of fats and calories and a low consumption of vegetables, fruits, and fibers.

Methods

Design and participants

Details of the general design of the study have been published elsewhere [9]. Briefly, a random-sample population-based study, the Córdoba Obesity and Diet Study (CODIES), was conducted in Córdoba, the capital city of the state of Córdoba (located in the center of Argentina). According to the last census, Córdoba city has a population of about 1,300,000 inhabitants and is the second largest city in Argentina.

The survey was conducted from January 2005 to December 2012, and a multistage population random sample was drawn. Using socio-economic indicators of the National Institute of Statistics and Census (INDEC), the city was divided into 12 homogeneous blocks. Then barrios, blocks, and streets on the blocks were randomly drawn, and finally, one to five houses in the street were selected. All participants were 18 years old or older. As suggested by a study of obesity in Latin American countries [12], sample size was calculated for a hypothetical overweight prevalence of 35 % with a probabilistic type I error <0.05 and a type II error of 0.10. Sample size was estimated to be 4567 subjects.

All participants gave informed consent prior to their inclusion in the study. The study was approved by the Ethical Committee of the Faculty of Medical Sciences, University of Córdoba, and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.

Demographic characteristics and lifestyle habits of the participants were probed through ad hoc questionnaires, and anthropometric measurements were taken directly. Dietary habits were probed by means of a recently validated Food Frequency Questionnaire, which includes qualitative and quantitative information on food intake [13]. Participants were asked to indicate the frequency they had eaten each particular item over the previous 12 months and the portion size (three categories: small, medium and large). For single-unit items (such as slices of bread or non usual foods), participants were asked to state how many units were eaten on each occasion. Three-dimensional portion size images or a validated photographic atlas based on standard portion sizes in Argentina was used to facilitate the participant’s quantification process [14]. Transformation of frequency of intake and portion size into nutrients (g or mg/day) was performed using Nutrio software [15]. Physical activity was explored using a validated short form of the International Physical Activity Questionnaire, adapted for Argentina [16] and expressed as number of metabolic equivalent (MET)-min/week. Socio-economic status (SES) was built as a composite indicator following the Argentine Marketing Association’s guidelines, which include four domains: educational level, job, housing, and amenities [17].

Statistical analysis

The descriptive statistical analysis was performed separately for categorical and continuous variables. Categorical variables were analyzed by the Chi-square test. For analytic goals, the following variables were categorized: BMI (normal/underweight: 25.0; overweight: 25.0–29.9; obesity: ≥30.0), age in years (<40, 40–59, ≥60), SES (low, medium, high), physical activity (PA) (insufficient: <600 MET-min/week; sufficient: 600–1500 MET-min/week; high: >1500 MET-min/week), and waist circumference (risk of metabolic and cardiovascular diseases: ≥94 cm for men and ≥80 cm for women).

Single foods were clustered into the following 15 groups: milk and yogurt, cheeses, meats and eggs, pulses, non-starchy vegetables, fruits, refined grains and starchy vegetables, whole grains, bakery products, candies (ice cream, chocolates, peanut butter, “dulce de leche”—milk and sugar caramel), added sugar and sweets (sugar, jam, honey), fats (butter, milk cream, vegetable oils and mayonnaise), alcoholic beverages, sugary drinks, and snacks (a small portion of food or drink, especially one eaten between regular meals with high energetic density and sodium).

The results of continuous variables are expressed as means (±SD) and categorical variables as percentages. In order to estimate mean intake differences of macronutrients and foods among categories of BMI, one-way analysis of variance was performed and the Bonferroni multiple comparison test was applied. Logistic regression analysis was performed to investigate associations between overweight/obesity and selected socio-demographic, physical activity, and dietary variables. Odds ratios (OR) and the corresponding 95 % confidence intervals (95 % CI) were estimated.

Macronutrients were categorized as follows: carbohydrate: low (<45), normal (45–65), high (>65) percent of daily total energy intake; proteins: low (<10), normal (10–35), high (>35) percent of daily total energy intake; total fats: low (<20), normal (20–35), high (>35) percent of daily total energy intake (g) [18]. Daily sodium intake was categorized following the maximum allowed intake of 2.3 g/day, as suggested by the National Academy of Sciences, Institute of Medicine, Food and Nutrition Board [19]. Single foods were categorized into tertiles following the distribution of the variables among low-/normal-weight subjects, without correction for mean energy intake.

Two models were estimated: The first included as the dependent variable overweight versus low-/normal-weight subjects, and the second included obese versus low-/normal-/overweight subjects. For each dependent variable, a model was first fitted without confounders and then a second which included these [Daily Energy Intake (as continuous variable), Gender, Age, SES and PA]. This analytic strategy was first applied to macronutrients as independent variables and then to foods. Food intake was previously categorized as tertiles of the specific food intake among low-/normal-weight subjects.

A p value <0.05 was considered as statistically significant. All statistical analyses were performed using Stata 12.1 statistical software [20].

Results

A total of 4368 out of 4467 sampled subjects (97.7 %) agreed to participate in the study. Data were inconsistent [extreme high (>7000) or low (<500) daily caloric intake] and incomplete (more than 10 % items missing) for 40 and 99 subjects, respectively, so the final sample size was 4328 subjects (58 % males). The cutoff for daily caloric intake seems to be reasonable as there were some obese subjects with daily caloric intake as high as 10,000 calories a day. Moreover, the same cutoff had been applied previously [9].

Sample mean age was 42.69 (±17.99) years and 51 % of the population was overweight. There was a general tendency to overweight and obesity as age increased. Overweight was present in 24.9, 42.0, and 43.0 % of those >40, 40–59, and 60 or more years old, respectively. Only 8.0 % of subjects under 40 years were obese, but this percentage was 26.5 % among those over 60.

Results of the descriptive analysis are shown in Table 1. Prevalence of overweight and obesity in this group was 34 and 17 %, respectively. Sixty percent of men and 45 % of women were overweight. There was a negative association between SES and BMI (p < 0.01): the higher the SES, the lower the prevalence of overweight.

Table 1 Socio-demographic and anthropometric characteristics in relation to body mass index of the subjects studied (n = 4328)
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Physical activity level was insufficient in 68 % (<600 MET-min/week) of the sample. Sixty-eight percent of overweight and 82 % of obese subjects did not reach a sufficient level of physical activity. Waist circumference showed that 47 % of the subjects were at risk of metabolic and cardiovascular diseases, including 77 % of overweight/obese people.

Table 2 summarizes the information regarding dietary exposure. Mean energy intake was 2853 kcal daily. There were statistically significant differences among BMI categories for mean intake of calcium, milk and yogurt, meats and eggs, pulses, refined grains and starchy vegetables, and candies. The multiple comparison provided by the Bonferroni test showed statistically significant differences among BMI categories for mean intake of carbohydrate, fiber and bakery products. There were no statistically significant differences in the mean intake of different food groups between genders and among SES categories (data not shown). There was a statistically significant difference between gender in alcoholic and non-alcoholic beverage intake (p < 0.01).

Table 2 Energy, macronutrients, and food intakes by body mass index of subjects studied (n = 4328)
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Risk analysis

Nutrients

Model: Overweight versus normal

There were lower ORs for normal (OR 0.6, 95 % CI 0.5–0.7) and high (OR 0.7, 95 % CI 0.5–0.9) tertiles of carbohydrate and for calcium intake (OR 0.9, 95 % CI 0.98–0.99) but only the effects for normal carbohydrate and for calcium remained when the estimates were adjusted for the set of confounders (Table 3).

Table 3 Overweight and obesity: unadjusted and adjusted ORs and 95 % CI of nutritional variables estimated from logistic regression models (LRM)
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Model: Obesity versus normal/overweight

There were lower ORs for the high (OR 0.4, 95 % CI 0.3–0.6) and normal (OR 0.6, 95 % CI 0.4–0.9) intake of fats and for normal intake of carbohydrate (OR 0.7, 95 % CI 0.5–0.9) in the unadjusted model, but only the effects for high intake of fats and normal intake of carbohydrate remained statistically significant in the adjusted model (p < 0.05). There was also a positive effect for sodium intake (OR 1.5, 95 % CI 1.1–1.8) in the adjusted model (Table 3).

Foods

Model: Overweight versus normal

There were lower ORs for the second and third tertiles of candies (OR 0.7, 95 % CI 0.6–0.9; OR 0.5, 95 % CI 0.5–0.7, respectively) and milk and yogurt (OR 0.71, 95 % CI 0.7–0.9; OR 0.7, 95 % CI 0.6–0.8, respectively) in the unadjusted model. These effects remained only in the third tertile of candies, milk, and yogurt in the adjusted model, but the second tertiles trend test was statistically significant (p < 0.05, Table 4).

Table 4 Logistic regression: OR and 95 % CI for foods
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In the adjusted model, the higher ORs were for third tertiles of sugar and sweets (OR 0.8, 95 % CI 0.6–0.9) and Sugary Drinks (OR 1.2, 95 % CI 1.03–1.5, trend p < 0.05); nevertheless, the second tertiles trend test of these foods was statistically significant (p < 0.05, Table 4).

Model: Obesity versus normal/overweight

There were higher ORs for both tertiles of refined grains and starchy vegetables (OR 1.7, 95 % CI 1.2–2.3; OR 2.4, 95 % CI 1.6–3.8) and the third tertile of snacks (OR 1.4, 95 % CI 1.1–1.8), and lower ORs for the third tertile of alcoholic beverages (OR 0.7, 95 % CI 0.5–0.9) and both tertiles of sugar and sweets (OR 0.6, 95 % CI 0.4–0.7; OR 0.7, 95 % CI 0.5–0.8), candies (OR 0.7, 95 % CI 0.6–0.9; OR 0.7, 95 % CI 0.5–0.9), milk and yogurt (OR 0.8, 95 % CI 0.6–0.9; OR 0.6, 95 % CI 0.5–0.8) and pulses (OR 0.7, 95 % CI 0.6–0.9; OR 0.8, 95 % CI 0.6–0.9) in the adjusted model, all with statistically significant trends.

Discussion

This is the first study of eating habits in urban populations of Argentina and their association with the current prevalence of overweight and obesity. Some demographic characteristics associated with categories of BMI had been identified in the same population [9].

In this study, the prevalence of obesity was 17 % and of overweight 51 %. Overweight (overweight vs normal) was negatively associated with fats, calcium, sugar, and sweets, candies and milk, and yogurt intakes and positively associated with sugary drinks. Obesity (obesity vs normal/overweight) was found to be negatively associated with fats, carbohydrates, sugar and sweets, alcoholic beverages, milk and yogurt, and pulses intakes and positively associated with sodium, refined grains and starchy vegetables, and snacks intakes.

Overall, 60 % of men and 45 % of women were overweight, while obesity was 17 %. There was, however, a significant difference between genders, with a higher prevalence of women among obese subjects. Previous studies about overweight and obesity prevalence in Argentina have been conducted. Although the methodological approach was different, higher rates of overweight (about 55 %) and obesity (26 %) were found [21, 22] in two small cities. The 2007 National Survey of Health and Nutrition found a prevalence of overweight in women aged 19–49 years of 44.3 % (24.9 % overweight and 19.4 % obese) [23], and in the first and second National Survey of Risk Factors (2005 and 2009), more than half the population (53.4 %) had overweight [24]. In our study, while overweight women had a prevalence similar to that reported nationally, the distribution of overweight and obesity was different: 28 and 17 %, respectively.

Worldwide, as mean BMI has increased, there has been an increasing tendency to obesity [8]. Overweight and obesity prevalence in Córdoba was lower than in the USA (33.8 and 68 %, respectively) but higher than in European countries [25]. In Latin America, there is a data conflict, as some studies have identified Argentina as having an overweight prevalence of less than 50 % [26] while others have reported that more than 15 % of Argentineans are obese as is the case in Colombia, Paraguay, and Uruguay [27].

The two keys for controlling or reducing weight are diet and exercise. A sedentary lifestyle is the main cause of weight gain [28, 29]. As in other South American populations [30], 67 % of the individuals in this study did not engage in sufficient physical activity even though the WHO reports that a sedentary lifestyle is the fourth attributable risk factor for death [7]. The protective effect of a high or sufficient level of physical activity was already shown in this population [9], and the results of this study agree with cross-sectional and prospective studies, which have highlighted the strength of the association of low levels of physical activity with obesity [31–33].

Macronutrients

In this population, the features mentioned above, coupled to excessive caloric intake, resulted in weight gain. Male and female median energy intakes were higher than the reference values [18]. Excessive caloric intake has been related with overweight and obesity in some cases [34, 35], but in others, this association has been found only with overweight [36]. It is worth noting that underreporting of dietary intake of obese people could cause the lack of association between intake and obesity, as has been documented [37, 38].

Even though diet is accepted as a determinant factor of overweight and obesity, the role of certain nutrients, especially fats [39, 40], is controversial, partly due to the impossibility of detecting low effects of nutrients, such as their biological interactions with other metabolic factors, which makes the measurement of individual effects difficult [41]. Carbohydrates can cause high-energy intake and later weight increase [42]. In this study, the average percentage of energy provided by carbohydrates was 53.08 %, similar to that reported for the southeastern US population [43]. The high intake of lipids in Argentinean diet is probably due to consumption of meat and poultry (27 and 8.6 g/person/day respectively), the lipid content of which (23.6/100 g from meat, 8.7/100 g from poultry) [44] provides half the recommended daily intake of this nutrient. This pattern has been described in other studies conducted in Córdoba [45]. This increased consumption of animal protein has been associated with higher risk of obesity [43], but this has not been evidenced in our study. It is worth noting that meat and dairy products come from a mix of feedlot and pastoral system animals [46].

Great attention has been paid in recent years to the effect of dietary calcium on weight and body composition. Over the past decade, some studies have suggested an effect of calcium on energy and lipid balance, highlighting the importance of an adequate intake of dairy products [47]. Although in this study, calcium intake was associated only with overweight, there was a negative association between milk and yogurt intake not only with overweight but also with obesity. Other components of dairy products are probably important in the energy balance. These include certain peptides with inhibitory action on the angiotensin-converting enzyme [48], conjugated linoleic acid [49], and branched-chain amino acids [50]. Other studies have investigated the amount of dairy intake in relation to the development of obesity and found that the relative risk of developing obesity was reduced by 18 % for each unit of increase in the amount of dairy products consumed [51].

A higher intake of fats and alcohol may contribute to the development of obesity, but this has not been evidenced in our study. However, higher intake of fat meats and alcohol is very common in Cordoba city [10, 11]. There is evidence that current and lifetime consumption of alcohol is positively associated with general and central obesity in both genders, regardless of the social and behavioral characteristics [52]. In our study, all individuals, regardless of their nutritional status, drank an average of 100 cc/day of alcoholic beverages, with significant differences between genders. Moreover, a negative association was found when ingesting less than 100 cc/day.

Foods

The beneficial effect of the inclusion of fiber in the diet is well known, as it can reduce energy intake and help prevent or treat obesity. Probably the fiber content of foods, together with their content of lipids and proteins, can directly influence overweight and obesity by its contribution to the glycemic index (GI). In effect, some studies have indicated that eating low-GI foods resulted in a 0.5 kg reduction in body fat compared with individuals who ingest high-GI foods [53]. In the present study, the mean intake was 20 g/day and 50 % of subjects consumed less than 18 g/day of fiber, which increased to 22 g/day in high SES. That consumption is less than the recommended intake of fiber for Argentina’s population (25–30 g/day) [54]. International studies reveal differences in consumption by gender. Chilean women who consumed more fiber presented a positive health effect [55], but this effect was not found in our study, probably due to the similarly low consumption of fiber among all study subjects.

Evidence suggests the potential beneficial effects of the slowly digestible and resistant fiber contained in whole grains, pulses, and vegetables [56]. Their intake in the Córdoba population was very low, particularly the consumption of pulses, which was less than 10 g/day, and even less at 6 g/day in subjects with obesity. In contrast, the mean intake of refined grains and starchy vegetables was greater than 260 g/day and among people with obesity was close to 300 g/day. The intake of whole grains was not more than 25 g/day. The reduced risk of chronic diseases among persons who consumed pulses and whole grains is probably related to the low GI. Low-GI foods cause a lower concentration of circulating insulin due to decreased postprandial glucose response [56]. The low insulin secretion steadily reduces the levels of free fatty acids, improves cellular metabolism of glucose [55], keeps blood glucose levels nearer the lower limit despite continuous glucose absorption from the intestine, and avoids the lipogenic effect caused by high-GI foods [42].

A negative correlation between the consumption of fruit and non-starchy vegetables and BMI in adults has been reported [30, 57]. A US study in adults found that people with a high consumption of fruits and non-starchy vegetables had a lower prevalence of obesity [58]. In this study, consumption of fruits and non-starchy vegetables was very low and similar to that observed in other studies [8, 59–61]. This is probably due to the fact that a large part of the population of Córdoba City has a low income and less access to food such as fruits and non-starchy vegetables, leading to a lower intake of vitamins, minerals, fiber, and water.

It has been shown that sugary drinks do not produce satiety in a similar way to that of solid foods with an equivalent content of carbohydrates [62]. An increased consumption of sugary drinks has been found to be associated with weight gain [63, 64], as was found in our study. The high content of fructose in these beverages may play a role in this mechanism. The amount of fructose in fruits is relatively small compared with that in sugary drinks [65], Sugary drinks have added corn syrups, which contain higher concentrations of fructose. Fructose has been implicated as a possible independent risk factor for the development of obesity [66] and has been linked to central adiposity and hyperlipidemia [67].

Studies on obesity and the influence of dietary factors have established that snack consumption is positively associated with overweight and obesity [36]. Moreover, other studies have reported that among middle-aged men, the frequency of snack consumption was positively and significantly related to insulin resistance [68]. Snacks are high-energy foods and the different preservatives that they contain potentiate the effects of other components [69]. The choice of these foods and the consumption of bakery products, sugar, and sweets should also be considered from an anthropological point of view [56]. Generally, when individuals select these foods, sensory perceptions and pleasure are priorities [70]. Some studies suggest that intensely pleasurable emotions associated with food are coupled to neurological system activity on the basis of reward/motivation, emotional (limbic), and warning processes [71]. Other clinical studies suggest that the most desired foods are those containing fat, sugar, or both [72]. There is thus a connection between the consumption patterns of snacks, the increase in the energy density of foods consumed and altered satiety, which together may play a role in obesity.

Methodological considerations

Some methodological issues need to be considered. This study was based on a population-based random sample of 4328 subjects which constitutes its principal strength. The sample is well suited to estimate with precision and validity the true prevalence of the two conditions. Underreporting of diet intake is an issue of overweight/obese people. However, if this bias is present in our study, this constitutes a non-differential misclassification of the exposure and the estimates should be biased toward the null [73]. Furthermore, the scenario where this study was conducted should be considered. The particular dietary pattern may put the Argentinean population in the upper tail of dietary exposure to proteins and fats and in the lower tail of non-starchy vegetables and pulses. Finally, this cross-sectional study does not permit causal inferences to be drawn but only finds associations.

Conclusions

Overweight and obesity are a public health threat in this population. A higher intake of sugar, fat and sodium and a lower consumption of vegetables, fruits, and whole grains characterize the dietary pattern of this population. Further studies are needed to better understand the mechanisms underlying this pattern and the high prevalence of overweight and obesity. Obesity management must take into account all these factors in planning programs of health promotion and the prevention of obesity and its complications.