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Journal of the American College of Nutrition, Vol. 24, No. 6, 456-465 (2005)
Published by the American College of Nutrition

Factors Associated with Obesity in an Adult Mediterranean Population: Influence on Plasma Lipid Profile

José Mataix, PhD, Magdalena López-Frías, PhD, Emilio Martínez-de-Victoria, PhD, María López-Jurado, PhD, Pilar Aranda, PhD and Juan Llopis, PhD, FACN

Institute of Nutrition and Food Technology and Department of Physiology, University of Granada, E-18071 Granada, SPAIN

Address reprint requests to: Juan Llopis, PhD, Instituto de Nutrición y Tecnología de Alimentos, Universidad de Granada, C/Ramón y Cajal 4, E-18071 Granada, SPAIN. E-mail: jllopis{at}ugr.es


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 ACKNOWLEDGMENTS
 REFERENCES
 
Objective: The aim of the study was to identify factors associated with obesity, and their influence on plasma lipid profile in an adult Mediterranean population.

Design: The data were obtained from a cross-sectional epidemiological survey.

Setting: The study population resided in Andalusia, a western Mediterranean region in southern Spain.

Subjects: The survey was carried out with a random sample of 3421 subjects (1747 men, 1674 women) between 25 and 60 years of age. Blood samples were obtained for biochemical assays in a random subsample of 340 subjects (167 men, 173 women).

Interventions: Food consumption was assessed by 48-h recall. Height, weight, triceps, biceps, subscapular and suprailiac skinfolds, mid-upper arm, waist (WC) and hip circumferences, glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol and triglycerides were measured. Information about lifestyles was obtained with a questionnaire.

Results: Of the adult population we studied, 18.9% were obese (body mass index [BMI] ≥ 30 kg/m2). A larger proportion of men than women were overweight, but the opposite was found for obesity. Mean plasma lipid values were not modified significantly by obesity or lifestyle factors, and were within the normal range. Sex, age, physical exercise and lower educational level were associated directly with the risk of obesity, and smoking was associated inversely with the risk of obesity. In obese smokers WC and waist-hip ratio were larger, and levels of HDL-cholesterol were lower (p < 0.05) than in obese nonsmokers. Glucemia was higher in obese persons who consumed alcohol (p < 0.05) than in obese persons who did not consume alcohol. The risk of hypercholesterolemia and high levels of LDL-cholesterol was associated only with age, and the risk of low levels of HDL-cholesterol was associated only with high WC.

Conclusion: Our results provide an estimate of the prevalence of obesity in the adult population in southern Spain, and of the associated factors. Sex, age, leisure-time physical exercise and educational level appear to influence obesity. Only age and WC but not BMI were associated with a risk of dyslipidemia. No dietary associations were observed between energy or macronutrient intake and plasma lipid concentrations in overweight or obese persons.

Key words: Mediterranean region, adult, obesity, associated factors, plasma lipids


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 ACKNOWLEDGMENTS
 REFERENCES
 
Obesity is currently considered a common risk factor for many chronic diseases. The widespread increase in its prevalence in recent years, and its association with reduced life expectancy, have made obesity one of the most urgent public health problems.

While it is ultimately true that obesity is a result of energy intake exceeding energy expenditure, many factors influence this balance. For example, energy intake and expenditure, and hence the development of obesity, can be influenced by heredity, age, gender, race, level of education and socioeconomic level, physical activity, eating habits, and psychological factors.

Evaluations of the effects of excess weight on health should consider the distribution of body fat as well as the amount of adipose tissue. Abdominal fat has been associated with insulin resistance [1], hyperlipidemia and hypertension [2], certain types of cancer [3] and osteoporosis [4].

Economic development in Spain during recent decades has favored the appearance of social, cultural and dietary changes in this typically Mediterranean country [5]. The proportion of the population considered obese increased by approximately 5% in Spain between 1987 and 1997 [6].

The aim of this study was to investigate the prevalence of factors associated with obesity, and their influence on plasma lipid profile, in an adult population residing in the region of Andalusia (southern Spain). It is hoped that this information will be useful in developing future health interventions in the fight against obesity.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 ACKNOWLEDGMENTS
 REFERENCES
 
Participants
The data reported here were obtained within the framework of a wide-ranging study in the region of Andalusia [7], a 87,597-km2 western Mediterranean region in southern Spain with 7,305,117 inhabitants [8]. We undertook a cross-sectional epidemiological survey from 1998 to 2000, with a representative random sample of adults living in the region who were between 25 and 60 years old at the time of the study. Sampling was probabilistic and stratified, and took place in several stages. The primary sampling unit was cities and towns (municipalities), the secondary unit was homes, and the tertiary unit was individuals of either gender.

In each of the eight provinces (domains) that make up the region, the sample was distributed proportionately between men and women, and between three age groups: 25–39 years, 40–49 years, and 50–60 years.

In each domain, municipalities (the primary sampling unit) were stratified proportionately on the basis of the number of inhabitants (<10,000, between 10,000 and 100,000, and > 100,000 inhabitants). Municipalities were chosen randomly in numbers that reflected the proportion of inhabitants that satisfied the inclusion criteria within each domain and population level.

Homes (the secondary sampling unit) were chosen with a random walk procedure. In each home one person was chosen (tertiary sampling unit) with the Kish method [9] from among residents who fulfilled the sampling criteria. When the questionnaire could not be used at a given address because of an error in the address, absence of the individual chosen for participation, refusal to participate, or incapacity to participate, the individual was replaced as recommended in the method.

The population of inhabitants between the ages of 25 and 60 years at the time of the study was 2,946,228 [8]. The theoretical sample size was 3680 subjects for a sampling error of less than 5% and estimates at the 95% confidence level. The actual sample consisted of 3421 individuals (1747 men, 1674 women), for a participation rate with valid observations of 92.96%. Participants were asked if they had any acute or chronic illness, and were included if they were (or appeared to be) in good health; pregnant and breastfeeding women were excluded.

Blood samples were taken for biochemical analysis from a random subsample of 340 subjects (167 men, 173 women) who comprised approximately 10% of the sample.

Food consumption was assessed by a 48-h recall method in which participants recalled in an interview all foods consumed during the preceding 48 hours [10]. The data were obtained by 8 dietitians with the aid of an open questionnaire and photographs as a reference for portion size. Food intakes were converted into energy and nutrients with the help of the Spanish Food Composition Table [11]. The composition database was used under AYS44 Diet Analysis software from ASDE, SA (Valencia, Spain).

Information about lifestyle factors (smoking, alcohol use, time devoted to leisure-time physical exercise and level of education) was collected with a slightly modified version of the questionnaire developed by the National Health Survey [12]. The study protocol was approved by the Medical-Ethical Committee of the Health Council of the Andalusian Regional Government, and informed consent was obtained from each subject.

For anthropometric measurements, the subjects were shoeless and dressed in their underwear and a disposable gown. Body weight was measured with a portable digital scale (Tefal, Sensitive Computer 9202 series 2/0, France) with a precision of 0.1 kg, and height was measured with a portable stadiometer (Holtain Portable, London, UK) with a precision of 0.1 cm. Skinfolds of the triceps, biceps, subscapular and suprailiac were measured with skinfold calipers (Holtain, London, UK) with a precision of 0.1 mm. Measurements of the circumference of the arm, waist (WC) and hips were taken with a plastic tape measure (Holtain, London, UK) with a precision of 0.1 cm. All measurements were obtained in accordance with the techniques and recommendations of the International Biological Programme [13] by personnel suitably trained for this task.

Total body density (D) was calculated with the equations of Durnin and Womersley [14]:

Men: age (years)

Women: age (years)

where X = sum of biceps, triceps, subscapular, and suprailiac skinfold thicknesses.

The percentage of body fat was calculated from density with Siri’s equation [15]:

Analytical Techniques
Blood was collected in the morning after the participants had abstained from eating overnight. Sodium heparin was used as an anticoagulant, and the samples were centrifuged at 3000 x g for 15 min at 20°C to separate plasma. Aliquots were prepared for storage (–20°C or –80°C) until further analysis. Total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), triglycerides, and glucose were measured with commercial enzymatic colorimetric kits from QCA (Amposta, Spain). Seriscann Normal (ref 994148) (QCA, Amposta, Spain) was used for quality control measures.

Statistical Analyses
The experimental data were analyzed with Student’s t test for independent samples. Differences between percentage values were verified with an asymptotic test to compare independent binomial proportions, assuming a gaussian distribution given that sample sizes were large. Linear regression analysis was used to find bivariate correlations; Pearson’s correlation coefficient was calculated for 95% confidence levels. Multiple logistic regression analysis was used to estimate the degree of association between overweight, obesity, WHR (waist/hip circumference ratio), WC (waist circumference) and body fat (dependent variables) and gender, age, physical exercise, educational level, smoking and drinking. This method was also used to estimate the degree of association between biochemical parameters (dependent variables) and sex, age, physical exercise, educational level and smoking. All analyses were done with version 11.0 of the Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA). Differences were considered significant at the 5% probability level.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
ACKNOWLEDGMENTS
 REFERENCES
 
Table 1 shows the characteristics of the study population, and summarizes the anthropometric data, lifestyle factors, mean energy and macronutrient intakes, and the biochemical parameters analyzed here (glucose and lipid profile).


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  		Table 1. Characteristics of the Participants

 
Table 2 shows the plasma levels of glucose and lipid profile by age group, BMI, WHR, WC and percentage of body fat. In both genders all mean values were within normal range. Plasma lipid levels tended to increase with age, whereas glucemia tended to increase with age and increasing WHR.


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  		Table 2. Glucose and Plasma Lipid Profile by Age Group, BMI, WHR, WC and Body Fat

 
Table 3 shows caloric intake and the percentage of men and women with overweight and obesity, high WHR, high WC and high percentage of body fat according to gender, age and lifestyle factors.


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  		Table 3. Factors Associated with Overweight, Obesity, WHR, WC and Body Fat by Gender

 
Morbid obesity (BMI ≥ 40) was found in 1.2% of the population (0.9% of the men, 1.6% of the women). Only 0.6% of the men and 1.5% of the women were underweight (BMI < 18.5). Body mass index increased with age in both men and women, and correlated directly with age (r = 0.33, p < 0.01) and body fat (r = 0.50, p < 0.01).

In this study, leisure time physical activity was inversely correlated with BMI (r = –0.12, p < 0.01) and percentage of body fat (r = –0.18, p < 0.01). On average, nonobese persons (BMI < 30) spent more time exercising than did obese persons (p < 0.01).

In the population of alcohol consumers, mean WHR values were higher than in participants who did not consume alcohol (0.84 ± 0.11 vs. 0.88 ± 0.12; p < 0.001). Biochemical analyses showed that glucemia was higher in obese persons who consumed alcohol (mean ± SD) (drinkers 102.00 ± 28.38, nondrinkers 96.11 ± 15.94 mg/dL; p < 0.05). In obese drinkers, glucemia correlated with BMI (r = 0.51, p < 0.05) and alcohol consumption (r = 0.45, p < 0.05).

In both sexes the percentage of the population with overweight or obesity was lower among smokers despite their greater energy intakes (Table 3), and despite the fact that male smokers spent less time exercising (p < 0.01).

When we compared obese persons (BMI ≥ 30) who were smokers or nonsmokers, we found that BMI was the same in both populations (BMI = 33.6), although smokers with obesity had a larger mean WC (105.56 ± 11.73 cm) than nonsmokers with obesity (101.98 ± 11.09 cm; p < 0.01), and a larger WHR (0.94 ± 0.11 vs. 0.88 ± 0.09; p < 0.001). In smokers with obesity, WC and WHR correlated significantly with the number of cigarettes smoked per day (r = 0.20 and r = 0.26, respectively; p < 0.01 in both cases). Moreover, smokers with obesity had lower levels of HDL-C (65.2 ± 17.64 vs. 51.7 ± 7.57 mg/dL; p < 0.05) than did nonobese smokers.

Table 4 shows the associations (multiple logistic regression) of overweight, obesity, WHR, WC and body fat with sex, age and lifestyle factors in adults.


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  		Table 4. Factors Associated with Obesity, Overweight, High WHR (Waist/Hip Ratio), High WC and High Body Fat after Multiple Logistic Regression Analysis

 
Table 5 shows the associations (multiple logistic regression) for plasma glucose and lipid profile with gender, age and lifestyle factors.


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  		Table 5. Factors Associated with Glucose and Plasma Lipid Profile after Multiple Logistic Regression Analysis

 
Logistic regression analysis adjusted for sex, age and lifestyle factors detected a significant association only between WC and the risk of low levels of HDL-C [OR = 3.48, 95% CI (1.06–11.38), WC men ≤ 102 cm and women ≤ 88 cm being designated 1 as the reference]. We found no significant associations for overweight, obesity or body fat with any of the biochemical parameters.

We found no statistically significant associations between plasma lipid levels and the intakes of energy, protein, total fat, saturated, monounsaturated or polyunsaturated fatty acids, or carbohydrates.


   DISCUSSION
 
Mean values for weight, height and BMI (Table 1) in our population were similar to those described in studies of other regions of Spain [16]. Compared to the reference values for a central European population [17,18] mean height for the Andalusian population was lower, while weight and BMI was generally higher.

The prevalence of obesity (Table 1) in our population was similar to that in other parts of Spain, but higher than the mean values found for the country in general [6,17], and higher than in some European countries such as Switzerland [18], France [19,20], The Netherlands [20] and Sweden [21,22]. However, the values we found were similar to those reported for populations in Naples (Italy) [20], Germany [23], Finland [24] and England (for women) [25], and lower than in the province of Latina (Italy) [20], the United States [26], England (for men) [25], Austria [27] and Saudi Arabia [28]. Geographic variations in obesity rates are an epidemiologic feature of comparisons both within and between countries, and appear to be associated with wealth only in very poor countries, but not in others [29].

Obesity was more prevalent in women than in men, whereas in men the prevalence of overweight was higher (Table 1). This distribution of obesity has often been observed in western countries [20,26]. Among women, parity has been identified as a predictor of weight gain. Among men, the main factor associated with weight gain seems to be the transition from an active lifestyle during adolescence (physical exercise, sports, etc.) to a more sedentary lifestyle. Earlier studies in several other countries also reported finding gender differences in obesity rates [20,23,30]. However, in other developed countries neither gender appears to be associated with a higher prevalence of obesity [24].

Although the distribution of obesity has been widely documented, the distribution of WHR values in different countries is less well studied [24]. Mean WHR values observed in the population we studied (Table 1) were slightly lower than mean values for Spain [16], and were similar to the mean values observed in populations that took part in the WHO MONICA European Project [31].

The development of the Spanish economy in recent decades and the consequent social and cultural changes have transformed dietary habits in this typically Mediterranean country. These changes are characterized by a decrease in consumption of grain products, potatoes, green vegetables and legumes, together with an increase in the consumption of meat, fruit and dairy products [5,7]. This tendency to move away from the so-called Mediterranean diet was reflected in the high intakes of protein and fat, along with the low intakes of carbohydrates (Table 1).

Mean values for plasma TC (Table 1) were higher than those reported for other parts of Spain [5]. However, they were lower than the values for the populations analyzed in the WHO MONICA European Project, with the exception of Poland, Russia and Sweden [32], and slightly higher than the values reported for the USA [33]. Our mean HDL-C values were higher than those found for the adult population of England [25], the USA [33] and Brazil [34], adult male population in Germany [23] and France [33], and lower than the values observed for the adult female population in France [35].

Although obesity has been associated with dyslipidemia [36], we found no significant differences in lipid profile between normal, overweight and obese persons (Table 2). In fact, all mean values for all three subgroups were within normal limits, although mean values for TC and LDL-C were above the ranges considered optimum (200 mg/dL for TC and 100 mg/dL for LDL-C). An earlier study of the obese population in the southeastern Spain (Region of Murcia) found normal lipid levels [37], and the authors suggested that the findings may be associated with the higher monounsaturated fat content (accounting for 50% of the fat intake, mainly oleic acid) and the lower saturated fat content in the diet. However, in the present study we found no association between monounsaturated fatty acid intake and plasma concentrations of HDL-C or LDL-C in persons who were overweight or obese.

The borderline-high levels of LDL-C in our study population may be related mainly with saturated fat intake, which was clearly higher than the recommended level, comprising more than 7% of the total calorie intake (Table 1) [38]. This might favor an increase in cerebrovascular and ischemic heart disease in this region. Despite the high mean values for HDL-C (Table 1) [38], mortality from these diseases increased in Andalusia at a rate of 6 per 100,000 inhabitants between 2002 and 2003 [8]. It should be recalled, however, that we found no significant association between energy or macronutrient intake and plasma lipid levels.

Abdominal obesity is currently considered an important risk factor for cardiovascular disease and type 2 diabetes [39]. Because WC and WHR are the most frequently used anthropometric indicators in epidemiological studies to determine visceral obesity [34], we suspect that the higher values for glucemia in persons with a high WHR (Table 2) may be related with insulin resistance caused by visceral fat, and with the higher risk of type 2 diabetes in this subpopulation [40].

The increase in the prevalence of obesity, abdominal obesity and body fat with age in adults has been widely documented [18,24,25,34], and a similar pattern was found in our study population (Table 3) and in other parts of Spain [6]. The correlation between BMI and age (see Results) and the results of logistic regression analysis (Table 4) confirmed these associations. Moreover, age was also associated with the risk of hypercholesterolemia and high LDL levels (Table 5), as others have also reported [41].

As in earlier research [24], leisure time physical activity in the population studied here was inversely associated with BMI. However, one study failed to find an association between physical activity and BMI [42], this finding may be related with differences the intensity and type of exercise. It was recently suggested that fat oxidation rates depend on the intensity and type of exercise, increasing from low to moderate intensities and being higher during running [43]. In our study population, active running and cycling are the types of exercise practiced most often with moderate intensity. Logistic regression analysis supported these associations by showing that physical exercise decreased the risk of obesity and abdominal obesity (Table 4).

Educational level can influence tendencies with respect to nutrient intakes [44]. The lower percentage of obese individuals among both men and women who belonged to the university-educated subgroup (Table 3) was the result of longer times spent exercising per week rather than differences in food intake. This finding may reflect a better awareness of the harmful effects of obesity and greater value placed on being thin in persons with higher levels of education. Logistic regression analysis showed that lower educational level was associated with an increased risk of obesity (Table 4). The inverse association between level of education and obesity has also been reported by other researchers [24].

Earlier studies that looked at the relationship between BMI and alcohol intake were inconclusive. Some authors observed direct or inverse correlations, whereas others failed to find any association. Curiously, some research has found a direct correlation between BMI and drinking only in men, but an inverse correlation in women [45]. In the present study we did not find significantly higher percentages of obesity in men who drank despite their higher energy intake (Table 3) and shorter time spent exercising (p = 0.001). However, the percentage of women who were obese was smaller among drinkers despite their higher calorie intake (Table 3). We suspect that these findings are related with the shorter times devoted to physical exercise among women who did not drink, although the difference in comparison to women who did drink was not statistically significant. Among women who did not drink we noted a higher percentage of inactive persons (78%) compared to women who drank (69%). It has also been suggested that in women, alcohol has less of an effect on body composition than it does in men [46].

The higher values for glucemia in the subpopulation of obese drinkers (see Results) and their greater risk of hyperglycemia in persons who drank (Table 5) may be related with insulin resistance caused by alcohol and abdominal obesity. Because mean alcohol consumption in this subpopulation was moderate (approximately 1 drink per day in women and 2 drinks per day in men, and the repercussion of alcohol on daily energy intake was thus minimal), in our opinion, the higher risk of hyperglycemia was related more closely with the high WHR seen in drinkers than with the amount of alcohol consumed.

Some research has found smoking to be associated with a lower BMI [24]. In the present study, despite the higher energy intake in smokers (Table 3), the percentages of obesity, overweight and body fat were lower in this subgroup (Table 4). This may be a result of the faster fat oxidation in smokers, related in turn with nicotine uptake [47], and to increased resting energy expenditure [48]. The results of logistic regression analysis supported the lower risk of obesity among smokers. However, in the subgroup of smokers with obesity had a larger WC and WHR. It has been suggested that smoking is associated with abdominal obesity [49], an association which increase the risk of chronic diseases [1–4]. Moreover, obese smokers had lower levels of HDL-C, a finding that also may be related with the higher abdominal obesity associated with cigarette smoking. Logistic regression analysis confirmed that high WC was significantly associated with a risk of low levels of HDL-C (see Results).

Despite the complexities that characterize comparison of the data and interpretation of the findings from cross-sectional surveys, our findings provide an accurate estimate of the factors currently associated with obesity in southern Spain. We found that the prevalence of overweight and obesity are high in this population, and that age, gender, physical exercise, level of education and smoking are associated with a risk of obesity. In general, the factors that made the greatest contribution to the rates of overweight and obesity were the age-related body changes, time spent exercising and educational level.

Although we noted a tendency toward higher basal plasma glucose concentrations in persons with a high WHR and obese persons who drink, mean plasma concentrations of lipids were not significantly associated with overweight, obesity, WHR or body fat. Of the factors analyzed in this report, only age and WC were significantly associated with the risk of dyslipidemia. These results may be related with lifestyle and dietary habits in southern Spain.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 ACKNOWLEDGMENTS
REFERENCES
 
We thank the Escuela Andaluza de Salud Pública in Granada, the Dirección General de Salud Pública and the Health Council of the Andalusian Regional Government (Spain) for their support, and K. Shashok for translating significant parts of the manuscript into English.

Received October 20, 2003. Accepted July 19, 2005.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 ACKNOWLEDGMENTS
 REFERENCES
 

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