Background
Notwithstanding the growing attention to overweight due to its impact on public health and health care costs, overweight prevalences are still escalating into a global epidemic [
1]. The burden of diseases associated with overweight is huge: hypertension, dyslipidemia, insulin resistance, type 2 diabetes, coronary heart disease, ischemic stroke, osteoarthritis and certain types of cancers. Furthermore, the increase of a sedentary lifestyle, associated with the same range of health problems, even worsens the situation in developed countries [
1].
To manage the overweight epidemic, it is essential to understand the complex processes leading to the excess of adiposity. These processes involve interactions of numerous factors, including genetic predisposition, social, cultural, environmental, and behavioural factors [
2,
3]. Although it is commonly accepted that genetic aspects contribute significantly to the variability in body fatness [
4], changes in lifestyles and in the environment over the last decades are probably the most important cause of the overweight epidemic [
5]. A number of studies have investigated the relationship between socio-demographic and socio-economic factors on the one hand, and overweight on the other hand. Age was found to be significantly associated to overweight [
6‐
9]. In industrialized countries a lower socio-economic status is associated with a higher risk of overweight in women, with a less apparent relationship in men [
6,
10‐
12].
Several studies aiming to determine overweight inducing factors, investigated the association of overweight with lifestyle behaviours such as smoking [
13‐
15], alcohol [
16‐
18], dietary habits and physical (in)activity [
19‐
25]. The determinants of overweight are found to be multifactorial and gender specific but the findings are inconsistent among studies. Epidemiological studies, assessing the relationship between alcohol consumption and BMI, revealed contradictory results. Some researchers concluded that alcohol consumption may contribute to overweight [
16‐
18], whereas in another study it was found that moderate alcohol consumption may have a protective effect on overweight [
18]. Studies focusing on the association between body weight and smoking generally concluded that body weight seems to be the highest in former smokers, the lowest in current smokers and medium in never smokers [
13‐
15]. A number of studies, but not all, have established that physical activity is inversely associated with body weight, and these results are less consistent for women than for men [
19,
20,
26]. However, there is substantial evidence that the level of physical activity is associated with overweight and it has been suggested that increasing levels of sedentariness, such as TV watching and computer use, have played a major role in the development of the current overweight epidemic [
5,
21‐
25]. Sedentary behaviour has been found to increase the risk of overweight [
22‐
24] and type 2 diabetes [
22].
Although BMI is an imprecise measurement of fatness [
27‐
29], most studies investigating the association between overweight and the potential related factors, used BMI to define overweight. In a preliminary study (N Duvigneaud et al. – unpublished data), a BMI of 30 kg/m
2 was shown to have insufficient sensitivity to screen for excess body fat in Flemish adults. A BMI ≥ 25 kg/m
2 and a waist circumference (WC) ≥ 94 cm for men and ≥ 80 cm for women show a better sensitivity. Furthermore, several authors have suggested the combination of BMI and WC as a diagnostic tool for overweight and health risk [
30,
31].
The main objective of the present study was to investigate the association of several socio-economic and lifestyle factors with overweight in Flemish adults, using BMI ≥ 25 kg/m2, WC ≥ 94 cm (men) or ≥ 80 cm (women), and the combination of BMI and WC for identifying overweight.
Results
The descriptive characteristics of the sample are presented in Table
1. Men have significantly higher mean BMI and WC than women. Males participate more in health related sports, work longer and watch more television than females. Flemish females spend more time in leisure time physical activities compared to males.
Table 1
Descriptive characteristics (mean ± SD or percentages) in Flemish men and women
Age (yrs) ** | 2595 | 47.68 (14.77) | 2308 | 46.59 (13.81) |
BMI (kg/m2) *** | 2595 | 25.82 (3.52) | 2308 | 24.79 (4.15) |
WC (cm) *** | 2595 | 90.88 (10.56) | 2308 | 79.06 (10.49) |
Health related sports (h/week) *** | 2595 | 3.66 (4.43) | 2308 | 2.40 (3.38) |
Total leisure time PA (h/week) *** | 2595 | 18.80 (10.92) | 2308 | 22.90 (10.06) |
TV viewing (h/week) *** | 2595 | 15.81 (9.01) | 2308 | 14.89 (8.74) |
| N | % | N | % |
Alcohol consumption †† | | | | |
Never | 125 | 4.8 | 215 | 9.3 |
1–3 drinks/day | 1738 | 67.0 | 1910 | 82.8 |
≥4 drinks/week or WEday | 596 | 23.0 | 169 | 7.3 |
≥4 drinks/every day | 136 | 5.2 | 14 | 0.6 |
Smoking status †† | | | | |
Never | 1326 | 51.1 | 1603 | 69.5 |
Former | 771 | 29.7 | 338 | 14.6 |
Current | 498 | 19.2 | 367 | 15.9 |
Education NS | | | | |
Primary | 675 | 26.0 | 617 | 26.7 |
Secondary | 823 | 31.7 | 677 | 29.3 |
College/university | 1097 | 42.3 | 1014 | 43.9 |
| BMI ≥ 25 kg/m2
| WC ≥ 94 cm | BMI ≥ 25 kg/m2
| WC ≥ 88 cm |
Age category | (%) | (%) | (%) | (%) |
18–34 y | 30.7 | 10.6 | 21.3 | 17.3 |
35–54 y | 57.2 | 35.1 | 37.8 | 36.9 |
55–75 y | 72.9 | 58.5 | 61.1 | 65.9 |
About 5% of the males and 9% of the females never drink alcoholic beverages. Most Flemish participants (males: 67%, women: 82.8%) drink moderately. More than a quarter of the men are infrequent or frequent heavy drinkers. In women, this percentage was found to be 7.9%. More male participants are ex-smokers or current smokers compared to females. More than 40% of the subjects of both genders have a college or university degree. The overweight prevalence by age category according to the BMI (≥ 25 kg/m2) and WC (≥ 94 cm for men or ≥ 88 cm for women) cutoffs for overweight are also presented in Table
1. The prevalence of overweight increases with age category and is largely above the 50% in the oldest age category of both genders.
The adjusted OR's for the likelihood of being overweight by socio-economic and lifestyle variables in Flemish men are presented in Table
2. In the first model, BMI ≥ 25 kg/m
2 was used to define overweight. In this model, each additional year from 18 to 75 years multiplies the risk of overweight by a factor 1.04. This model also reveals that males drinking 1 to 3 drinks/day (OR = 0.62), males having a college or university degree (OR = 0.69) and males participating in health related sports for more than 4 h/week (OR = 0.79) have significantly lower OR's with regard to overweight. On the other hand, males who stopped smoking (OR = 1.59) and males who spend more than 11 h/week (OR = 1.58) watching TV have significantly higher OR's for being overweight compared to the reference category.
Table 2
Odds ratios for the likelihood of being overweight by socio-economic and lifestyle variables in men
Age | 2595 | 1.04 |
p < 0.001
| 2595 | 1.06 |
p < 0.001
| 2054 | 1.06 |
p < 0.001
|
Alcohol consumption | | |
p = 0.012
| | |
p = 0.032
| | |
p = 0.018
|
Never | 125 | 1.00 | (ref.) | 125 | 1.00 | (ref.) | 91 | 1.00 | (ref.) |
1–3 drinks/day | 1738 | 0.62 | (0.41–0.94)* | 1738 | 0.94 | (0.62–1.43) | 1379 | 0.71 | (0.43–1.16) |
≥ 4 drinks/week or WEday | 596 | 0.78 | (0.51–1.21) | 596 | 1.19 | (0.76–1.86) | 474 | 0.92 | (0.55–1.56) |
≥ 4 drinks/every day | 136 | 0.94 | (0.54–1.63) | 136 | 1.54 | (0.89–2.67) | 110 | 1.25 | (0.66–2.37) |
Smoking status | | |
p < 0.001
| | |
p < 0.001
| | |
p < 0.001
|
Never | 1326 | 1.00 | (ref.) | 1326 | 1.00 | (ref.) | 1036 | 1.00 | (ref.) |
Past | 771 | 1.59 | (1.30–1.96)*** | 771 | 1.71 | (1.40–2.10)*** | 615 | 1.94 | (1.54–2.44)*** |
Current | 498 | 0.88 | (0.70–1.11) | 498 | 1.08 | (0.84–1.38) | 403 | 0.96 | (0.74–1.26) |
Education | | |
p < 0.001
| | |
p = 0.201
| | |
p = 0.017
|
Primary | 675 | 1.00 | (ref.) | 675 | 1.00 | (ref.) | 533 | 1.00 | (ref.) |
Secondary | 823 | 0.97 | (0.77–1.23) | 823 | 0.88 | (0.70–1.11) | 632 | 0.91 | (0.70–1.19) |
College or university | 1097 | 0.69 | (0.55–0.87)** | 1097 | 0.81 | (0.64–1.02) | 889 | 0.71 | (0.54–0.92)** |
Health related sports (h/week) | | |
p = 0.080
| | |
p < 0.001
| | |
p = 0.009
|
Tertile 1 (<0.62) | 857 | 1.00 | (ref.) | 857 | 1.00 | (ref.) | 696 | 1.00 | (ref.) |
Tertile 2 (0.62 – 4) | 903 | 0.97 | (0.79–1.19) | 903 | 0.75 | (0.60–0.93)** | 703 | 0.82 | (0.64–1.04) |
Tertile 3 (>4) | 835 | 0.79 | (0.63–0.99)* | 835 | 0.61 | (0.48–0.77)*** | 655 | 0.66 | (0.50–0.86)** |
Total leisurte time PA (h/week) | | |
p = 0.399
| | |
P = 0.235
| | |
p = 0.163
|
Tertile 1 (<12.73) | 863 | 1.00 | (ref.) | 863 | 1.00 | (ref.) | 687 | 1.00 | (ref.) |
Tertile 2 (12.73 – 20.83) | 863 | 0.87 | (0.70–1.07) | 863 | 0.83 | (0.66–1.03) | 683 | 0.78 | (0.61–1.01) |
Tertile 3 (>20.83) | 869 | 0.91 | (0.72–1.15) | 869 | 0.86 | (0.68–1.10) | 684 | 0.85 | (0.64–1.12) |
TV viewing (h/week) | | |
p < 0.001
| | |
p < 0.001
| | |
p < 0.001
|
Tertile 1 (<11) | 820 | 1.00 | (ref.) | 820 | 1.00 | (ref.) | 657 | 1.00 | (ref.) |
Tertile 2 (11 – 19) | 931 | 1.58 | (1.29–1.94)*** | 931 | 1.45 | (1.16–1.81)** | 710 | 1.67 | (1.31–2.13)*** |
Tertile 3 (>19) | 844 | 1.54 | (1.24–1.92)*** | 844 | 1.80 | (1.43–2.27)*** | 687 | 1.97 | (1.52–2.54)*** |
In the second model, using WC ≥ 94 cm to determine overweight, educational level (p = 0.201) is not significantly associated with overweight. Alcohol consumption as a global factor is significant (p = 0.032), but none of the alcohol consumption levels reaches significance. Each additional year of age multiplies the risk of having WC ≥ 94 cm by 1.06. Similar to the first model, males who smoked in the past have 71% higher odds of being overweight than males who never smoked, and males watching TV more than 11 h/week also have significantly higher OR's compared to the reference category. Males in the second and third tertile of health related sports have significantly less chance of being overweight compared to males in the reference category, OR's 0.75 and 0.61 respectively.
In the third model, overweight was defined by the combination of BMI ≥ 25 kg/m2 and WC ≥ 94 cm. As in the second model, for each additional year the risk of overweight is multiplied by a factor 1.06, while the levels of alcohol consumption are not significantly associated with the likelihood of being overweight. According to this last model, former male smokers (OR = 1.94) and males watching TV more than 11 h/week (OR = 1.67) and more than 19 h/week (OR = 1.97) have also significantly higher OR's for being overweight compared to the reference category. In all 3 models, TLTPA is not significantly associated with overweight.
The adjusted OR's for the likelihood of being overweight by socio-economic and lifestyle variables in Flemish women are given in Table
3. In all three models, age is positively associated with the risk of being overweight. Each additional year of age from 18 to 75 years multiplies the risk of being overweight by 1.04 in the first model or by 1.06 in the second and third model. Alcohol consumption (p = 0.701) is not significantly associated with overweight in women. In the first model, females who are currently smoking (OR = 0.66), females having a secondary (OR = 0.74) or college/university degree (OR = 0.56) and females participating in health related sports more than 2.46 h/week (OR = 0.71) have significantly lower OR's for being overweight (BMI ≥ 25 kg/m
2) compared to the reference category. Females in the third tertile of TLTPA and females who spend more than 9 h/week watching TV have 29% higher odds of being overweight compared to the reference category.
Table 3
Odds ratios for the likelihood of being overweight by socio-economic and lifestyle variables in women
Age | 2308 | 1.04 |
P < 0.001
| 2308 | 1.06 |
p < 0.001
| 2019 | 1.06 |
p < 0.001
|
Alcohol consumption | | |
P = 0.701
| | |
p = 0.177
| | |
p = 0.289
|
Never | 215 | 1.00 | (ref.) | 215 | 1.00 | (ref.) | 190 | 1.00 | (ref.) |
1–3 drinks/day | 1910 | 0.91 | (0.67–1.24) | 1910 | 0.84 | (0.61–1.15) | 1664 | 0.87 | (0.62–1.22) |
≥ 4 drinks/week or WEday | 169 | 0.98 | (0.62–1.55) | 169 | 0.94 | (0.59–1.50) | 153 | 0.97 | (0.59–1.60) |
≥ 4 drinks/every day | 14 | 1.59 | (0.50–5.07) | 14 | 2.63 | (0.78–8.92) | 12 | 2.65 | (0.73–9.63) |
Smoking status | | |
P = 0.005
| | |
p = 0.053
| | |
p = 0.013
|
Never | 1603 | 1.00 | (ref.) | 1603 | 1.00 | (ref.) | 1394 | 1.00 | (ref.) |
Past | 338 | 1.06 | (0.83–1.36) | 338 | 1.15 | (0.89–1.48) | 295 | 1.14 | (0.87–1.51) |
Current | 367 | 0.66 | (0.51–0.86)** | 367 | 0.77 | (0.59–1.00) | 330 | 0.69 | (0.52–0.92)* |
Education | | |
P < 0.001
| | |
p = 0.001
| | |
p < 0.001
|
Primary | 617 | 1.00 | (ref.) | 617 | 1.00 | (ref.) | 531 | 1.00 | (ref.) |
Secondary | 677 | 0.74 | (0.59–0.94)* | 677 | 0.74 | (0.58–0.94)* | 582 | 0.70 | (0.54–0.91)** |
College or university | 1014 | 0.56 | (0.44–0.71)*** | 1014 | 0.62 | (0.48–0.80)*** | 906 | 0.55 | (0.42–0.72)*** |
Health related sports (h/week) | | |
P = 0.013
| | |
p = 0.005
| | |
p = 0.002
|
Tertile 1 (0) | 772 | 1.00 | (ref.) | 772 | 1.00 | (ref.) | 687 | 1.00 | (ref.) |
Tertile 2 (0 – 2.46) | 776 | 0.87 | (0.70–1.08) | 776 | 0.92 | (0.71–4.15) | 686 | 0.86 | (0.68–1.10) |
Tertile 3 (>2.46) | 760 | 0.71 | (0.57–0.89)** | 760 | 0.69 | (0.54–0.87)** | 646 | 0.63 | (0.49–0.82)*** |
Total leisurte time PA (h/week) | | |
p = 0.114
| | |
p = 0.045
| | |
p = 0.041
|
Tertile 1 (<22.49) | 765 | 1.00 | (ref.) | 765 | 1.00 | (ref.) | 692 | 1.00 | (ref.) |
Tertile 2 (22.49 – 39.56) | 772 | 1.10 | (0.88–1.38) | 772 | 0.91 | (0.72–1.14) | 672 | 0.97 | (0.76–1.25) |
Tertile 3 (>39.56) | 771 | 1.29 | (1.01–1.64)* | 771 | 1.21 | (0.94–1.54) | 655 | 1.31 | (1.00–1.72)* |
TV viewing (h/week) | | |
p = 0.079
| | |
p = 0.017
| | |
p = 0.010
|
Tertile 1 (<9) | 562 | 1.00 | (ref.) | 562 | 1.00 | (ref.) | 506 | 1.00 | (ref.) |
Tertile 2 (9 – 18) | 1014 | 1.29 | (1.02–1.62)* | 1014 | 1.41 | (1.11–1.79)** | 892 | 1.41 | (1.09–1.82)* |
Tertile 3 (>18) | 732 | 1.29 | (0.99–1.67)* | 732 | 1.35 | (1.04–1.77)* | 621 | 1.39 | (1.04–1.86)* |
In the second model, alcohol consumption (p = 0.177), smoking status (p = 0.053) and levels of TLTPA are not significantly associated with overweight defined by WC ≥ 80 cm. Similar to the first model, females with a secondary (OR = 0.74) or college/university degree (OR = 0.62) and females participating in health related sports activities more than 2.46 h/week (OR = 0.69) have less chance of being overweight compared to the reference category. Females in the second and third tertiles of watching TV have significantly higher OR's, 1.41 and 1.35 respectively, for being overweight compared to the females in the first tertile.
The third model, using the combination of BMI ≥ 25 kg/m2 and WC ≥ 80 cm shows results similar to model 1 in women.
Discussion
The main purpose of the present study was to determine the association of several socio-economic and lifestyle factors with overweight in Flemish adults, using BMI ≥ 25 kg/m2, WC ≥ 94 cm (men) or WC ≥ 80 cm (women) and the combination of BMI and WC for identifying individual overweight.
Although BMI has some limitations, most studies investigating the overweight associated factors used this index as sole indicator of overweight. One of the strengths of this study is the added use of WC, next to BMI, as an indicator of abdominal obesity. Although BMI shows a high positive correlation with WC in men (r = 0.91) and women (r = 0.90), different results were observed between the first (BMI) and the second model (WC) in both genders. In men, the models differ for education and health related sports, while in women they differ for smoking status and TLTPA. This finding indicates that BMI and WC have not the same discriminative function regarding the different lifestyle factors. The OR's based on the combined use of BMI and WC are somewhat more explicit. This may be due to the fact that the combined use of BMI and WC allows to distinguish between the overweight and non-overweight group more accurately because doubtful cases were excluded. All excluded men (20.8%) have a normal WC, but a BMI ≥ 25 kg/m2. Only 12.5% of the women were excluded, half of them for an increased BMI and a normal WC, the other half for a normal BMI, but an increased WC.
In agreement with the literature, age was positively related with overweight in both genders. The results of epidemiological studies on the association between alcohol intake and body weight are equivocal. A recent study of Breslow and Smothers [
17], examining the association between drinking patterns and BMI, revealed a positive association. Men and women who consumed the smallest quantity of alcohol per drinking day had the lowest BMI, those who consumed the greatest quantity had the highest BMI. The expectation that consumption of alcohol might be associated with overweight was not fully confirmed in the present study. Only frequent heavy drinkers (≥ 4 drinks/every day) are more likely to be overweight in both genders. The small number of frequent heavy drinkers in female subjects may have contributed to insufficient statistical power to detect significance. On the other hand, males consuming 1 to 3 drinks per day have a significantly lower OR (0.62) for overweight compared to never drinkers in the first model. Similar to our findings, other studies have reported that moderate drinking appears not to be positively associated with overweight in both genders [
18,
19,
43,
44]. An explanation for the U-shape relationship between alcohol intake and overweight may be that moderate drinkers of alcoholic beverages compensate for energy derived from alcohol by eating less [
17].
Smoking is usually associated with lower BMI. According to several authors, body weight appears to be the highest in ex-smokers, and the lowest in current and medium in never smokers [
13‐
15,
45]. Our results corroborate these findings. Using BMI and the combination method to define overweight, women who are current smokers have significantly lower odds for being overweight. However, this trend was not significant in men. Former smokers in Flemish men had significantly higher OR for overweight compared to never and current smokers in all 3 models. The same but not significant trend was observed in women. It is suggested that the weight gain associated with smoking cessation could be partly caused by the lack of nicotine as an appetite suppressant [
11]. Smoking cessation also leads to changes in adipose cell metabolism, in particular increases in adipose tissue lipoprotein lipase activity [
46,
47]. This process may also contribute to the increase in weight gain associated with smoking cessation. Given the well-known smoking health related risks, but also given the expected weight gain associated with smoking cessation, anti-smoking campaigns should especially target youth to prevent them to start smoking.
Overall, the prevalence of overweight and obesity in developed countries is higher in lower socio-economic groups [
9,
48‐
50]. In the present study, women with higher level of education (secondary or college/university diploma) are less likely to be overweight in all three models. As in other studies [
51‐
53], the relationship between education level and overweight was less consistent in men. It has been suggested that individuals with higher education tend to have healthier behaviours, including healthier dietary habits than those with low education [
15,
53,
54]. A higher educational level may act upon overweight through better knowledge of healthy food habits or through more comfortable budgetary conditions to buy healthy nutrients such as fruits and vegetables.
An explanation for the fact that females in the highest tertile of TLTPA are more likely to be overweight according to the first and the third model, may be that TLTPA also includes activities of low intensity (e.g. housekeeping) not affecting weight and body composition. On the other hand, TLTPA is not significantly associated with the likelihood of being overweight among Flemish men. Similarly, in the study of Santos et al. [
26] no significant contribution of total physical activity was found when comparing obese to normal weight participants. However, when only regular physical exercise was considered, obese participants of both genders were found to take significantly less exercise. Several authors have reported an inverse association of self-reported physical activity with obesity and increasing BMI [
19,
20,
26,
53]. Similar results are also observed in our study. Adults in the highest tertile of health related sports have significantly lower OR's for the likelihood of being overweight in all three models.
As in numerous other studies [
5,
21‐
25], our results indicate a positive association between watching TV/using computer and overweight. Flemish males watching TV/using computer more than 11 h/week and females watching TV/using computer more than 9 h/week have significantly higher odds for being overweight. Watching television could lead to overweight through reduced energy expenditure or through the association of television viewing with the consumption of snacks [
22]. Due to the limitations in mobility and the social isolation associated with overweight, it is also possible that overweight may lead to more TV viewing.
The present study has some limitations. The cross-sectional design of this study does not permit to infer causal relationships from our results. In addition, the use of questionnaires to assess habitual physical activity has been reported to be crude and imprecise, since it is a less objective measurement of physical activity than accelerometers [
55] or pedometers [
56,
57]. Notwithstanding this critique, the FPACQ used in our study was found to be a reliable and reasonably valid questionnaire for the assessment of different dimensions of physical activity in students [
38] and adults [
39]. Considering the fact that the development of obesity is mainly due to an imbalance between food intake and energy expenditure, the lack of data concerning dietary habits can also be considered as a limitation of this study.
In spite of its limitations, the present study provides unique data on the socio-economic and lifestyle factors associated with overweight in Flemish adults. Moreover, the data are from a large sample with a wide age range from 18 to 75 years. Another strength of this study is that body weight, height and WC were measured by trained staff and not self-reported. In addition, the combination of BMI and WC, aiming to reduce misclassification of individuals, is an interesting and novel approach to study associations between overweight and lifestyle factors. Combining BMI and WC can be seen as a statistical limitation because the continuity of a one-factor criterion is lost. However, it can also be taken as a methodological improvement as it leads to more contrasting groups. Some associations of lifestyle factors with overweight, found with the combination method, were not detected when using only one single criterion for overweight.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ND, LM, KW and PD participated in the data collection. WD, JL, RP and MT helped with the study design. ND analysed the data and wrote a first version of the manuscript. All authors provided comments on the drafts and assisted in editing the manuscript. They all read and approved the final version of the manuscript.