Epidemiology

Takeaway food consumption and cardio-metabolic risk factors in young adults

Abstract

Background/objectives:

Takeaway food consumption is positively associated with adiposity. Little is known about the associations with other cardio-metabolic risk factors. This study aimed to determine whether takeaway food consumption is associated with fasting glucose, insulin, lipids, homeostasis model assessment (HOMA) and blood pressure.

Subjects/methods:

A national sample of 1896, 26–36 year olds completed a questionnaire on socio-demographics, takeaway food consumption, physical activity and sedentary behaviour. Waist circumference and blood pressure were measured, and a fasting blood sample was taken. For this analysis, takeaway food consumption was dichotomised to once a week or less and twice a week or more. Linear regression was used to calculate differences in the adjusted mean values for fasting lipids, glucose, insulin, HOMA and blood pressure. Models were adjusted for age, employment status, leisure time physical activity and TV viewing.

Results:

Compared with women who ate takeaway once a week or less, women who ate takeaway twice a week or more had significantly higher adjusted mean fasting glucose (4.82 vs 4.88 mmol/l, respectively; P=0.045), higher HOMA scores (1.27 vs 1.40, respectively, P=0.034) and tended to have a higher mean fasting insulin (5.95 vs 6.45 mU/l, respectively, P=0.054). Similar associations were observed for men for fasting insulin and HOMA score, but the differences were not statistically significant. For both women and men adjustment for waist circumference attenuated the associations.

Conclusion:

Consuming takeaway food at least twice a week was associated with cardio-metabolic risk factors in women but less so in men. The effect of takeaway food consumption was attenuated when adjusted for obesity.

Introduction

Takeaway and fast food consumption has been shown to be positively associated with adiposity in study populations in the United States, Australia and Europe (French et al., 2000; Bowman and Vinyard, 2004; Satia et al., 2004; Pereira et al., 2005; Bes-Rastrollo et al., 2006; Schroder et al., 2007; Duffey et al., 2009; Smith et al., 2009). Although there are no standard definitions, fast food is the term used in North American and typically includes foods that can be obtained quickly such as fried chicken, burgers, fries and pizza. Takeaway is the common term used in Australia and includes fast food and other take out meal options such as Indian, Chinese and Thai food. Takeaway and fast foods are thought to contribute to adiposity through high energy density (Prentice and Jebb, 2003) and large portion sizes. In a nationally representative study of American adults, participants consumed an average of 205 more calories (858 kJ) on days when they ate fast food than on non-fast food days (Bowman and Vinyard, 2004).

In addition to being high in energy, takeaway and fast foods are often high in total and saturated fat and salt (Dunford et al., 2010). These dietary components have been shown to increase total and low-density lipoprotein (LDL) cholesterol and blood pressure, which are risk factors for cardiovascular disease and type 2 diabetes (Katan et al., 1994; Parillo and Riccardi, 2004). To our knowledge, only data from the Coronary Artery Risk Development in Young Adults (CARDIA) Study in the United States has been used to examine associations between fast food consumption and cardio-metabolic risk factors (Pereira et al., 2005; Duffey et al., 2009). The CARDIA study is a longitudinal study of >3000 participants who were aged 18–30 years at baseline. Participants who consumed fast food more than twice a week at baseline and at the 15 years follow-up had gained, on average, an extra 4.5 kg of body weight, and had a twofold greater increase in insulin resistance than those who ate takeaway less than once per week at both time points (Pereira et al., 2005). These associations remained significant after adjusting for socio-economic status and dietary intake, but other important potential confounders or mediators such as overweight and obesity, weight gain and physical activity were not considered for the insulin resistance analysis.

A more recent analysis of the CARDIA data examined associations between cardio-metabolic risk factors and fast food consumption over a 13-year follow-up period (Duffey et al., 2009). Participants in the highest quarter of fast food consumption at baseline had higher waist circumference, insulin resistance and triglyceride concentrations, and lower high-density lipoprotein (HDL) cholesterol concentrations 13 years later than those in the lowest quarter of fast food consumption. These associations remained significant after adjusting for socio-economic status, lifestyle factors and energy intake at baseline. Whether the associations with lipids and insulin resistance were independent of waist circumference were not reported.

We have previously shown takeaway food consumption to be associated with poorer diet quality and moderate abdominal obesity in young adult men and women (Smith et al., 2009). The aims of this study were to determine whether takeaway food consumption was associated with cardio-metabolic risk factors in young adults, and whether any associations were mediated through obesity.

Materials and methods

Study population

The Childhood Determinants of Adult Health (CDAH) Study is a follow-up of the 1985 Australian Schools Health and Fitness Survey, a nationally representative study of 8498 children aged 7–15 years (Dwyer and Gibbons, 1994). During 2001–2002, participants were traced and invited to attend one of the 34 study clinics around Australia during 2004–2006 at 26–36 years of age (Smith et al., 2009). Clinics were attended by 2410 participants and included anthropometric measurements and collection of fasting blood samples. The study was approved by the Southern Tasmania Health and Medical Research Ethics Committee, and all participants gave informed written consent.

Dietary assessment

Frequency of takeaway food consumption was assessed using the question, ‘how many times per week would you usually eat hot takeaway meals (for example, pizza, burgers, fried or roast chicken and Chinese/Indian/Thai takeaway)?’ Participants could choose one of the five answers ranging from ‘I don’t eat takeaway’ to ‘6–7 meals per week’. Chinese, Indian and Thai foods were included because although they can contain more vegetables than other takeaway items they are still often high in fats, salt and sugar. For analysis, the responses were dichotomised to less than twice per week or twice a week or more, as there were small numbers in the lowest and the two highest frequency groups. We have previously shown that takeaway food consumption using this short question is consistent with consumption of takeaway type foods from a food frequency questionnaire (Smith et al., 2009).

Blood chemistry

Venous blood samples were collected from the antecubital vein after an overnight fast. Triglycerides, total cholesterol, HDL cholesterol and glucose were determined enzymatically using an Olympus AU5400 automated analyser (Olympus Optical, Tokyo, Japan). LDL cholesterol concentration was calculated using the Friedewald formula (Friedewald et al., 1972). Two methods were used to determine fasting insulin concentration; microparticle-enzyme immunoassay kit (AxSYM, Abbot Laboritories, Abbort Park, IL, USA) and electrochemiluminescence immunoassay (Elecsys Modular Analytic E170, Roche Diagnostics, Mannheim, Switzerland). Measurements made using the microparticle-enzyme immunoassay were calibrated by applying a correction factor of 0.81, determined from a comparison of values for 31 samples assayed by each method. The inter-assay coefficient of variation ranged from 10.5 to 12.2% for the microparticle-enzyme immunoassay and 4.4 to 5.7% for the electrochemiluminescence immunoassay. Insulin sensitivity was estimated by the homeostasis model assessment (HOMA) index, calculated as fasting serum insulin (U/m) × fasting glucose (mmol/l)/22.5.

The use of lipid-lowering medication and the Third Report of the National Cholesterol Education Program (NCEP) Adult Treatment Panel guidelines (2002) were used to classify participants as having high triglycerides (2.26 mmol/l), total cholesterol (6.22 mmol/l), or LDL cholesterol (4.14 mmol/l) or low HDL cholesterol (1.1 mmol/l).

Blood pressure

Blood pressure was measured three times using a digital automatic monitor (Omron HEM907, Omron Healthcare Inc, Kyoto, Japan). The mean value was used in the analysis. High blood pressure was defined as systolic blood pressure 130 and <140 mm Hg or diastolic blood pressure 85 and <90 mm Hg. Hypertension was defined as the use of blood pressure-lowering medication, or systolic blood pressure 140 mm Hg or diastolic blood pressure 90 mm Hg (Heart Foundation, 2008).

Continuous metabolic syndrome score

A continuous metabolic syndrome score was created using the validated methods described by Wijndaele et al. (2006) and Schmidt et al. (2010). A continuous score was used to eliminate the need to dichotomise continuous outcomes and because cardio-metabolic risk increases progressively with increasing numbers of risk factors. Briefly, sex-specific principal components analysis with varimax rotation was applied to normalised International Diabetes Federation metabolic syndrome risk factors (International Diabetes Federation, 2005) to derive the principal components with Eigenvalues 1.0. Two principal components were identified in the CDAH sample that explained 34 and 26% of the variance in men, and 31 and 25% of the variance in women (Smith et al., 2010). These principal component scores were summed and weighted according to the relative proportion of variance explained to compute the continuous metabolic syndrome score. A higher score indicates an increased cardio-metabolic risk.

Covariates

Self-reported demographic variables included age, sex, highest level of education achieved (school only, vocational and university), occupation (professional/manager, non-manual, manual and not in workforce), employment status (working and not in the workforce) and marital status (married or living as married and other). Smoking was classified on the basis of self-report as never, former or current smoker. Leisure time physical activity was assessed using the long version of the International Physical Activity Questionnaire (Craig et al., 2003). Time spent watching TV was also assessed (Salmon et al., 2003). Waist circumference was measured three times at the narrowest point between the lower costal border and the iliac crest, at the end of normal expiration. The mean of the three measurements was calculated. Weight was measured using a portable scale (Heine, Dover, NH, USA) and height was measured using a portable stadiometer (Invicta, Leicester, UK). Body mass index (BMI) (kg/m2) was calculated. Accessibility/Remoteness Index of Australia classifications (major city, inner regional, outer regional and remote/very remote) were assigned to participants based on census collection districts. Usual daily intake of fruit and vegetables were assessed using short questions (Coyne et al., 2005; Smith et al., 2009). Daily fish consumption was calculated from three food frequency questionnaire items. Participants reported how often in the previous 12 months they had consumed canned, fresh and frozen fish. Daily equivalents were calculated and summed to give daily fish consumption (Smith et al., 2009).

Statistical analysis

Adjusted mean values for cardio-metabolic variables and differences in the adjusted means were calculated using linear regression. Covariates included in the adjusted analyses were those that were plausibly associated with the outcome, were not intermediates between the exposure and the outcome, and changed the coefficient of the variable for the principal study factor by >10% when included in the model (Greenland, 1989). Categorical variables were represented in the analyses as binary (0/1) covariates, and scaled covariates were used to represent continuous variables. The dependent variable was transformed where necessary to improve normality of residuals and reduce heteroskedasticity, and the scale of each continuous covariate was checked when included in each regression model. The models were adjusted for age, employment status, leisure time physical activity and (log-transformed) TV viewing. Daily fruit, vegetable and fish consumption was added to the models to examine whether any differences in cardio-metabolic risk factors were explained by differences in dietary intake. Waist circumference and BMI were added to separate models to determine whether the differences were mediated through abdominal obesity or overall obesity.

Log binomial regression was used to test for associations between takeaway food consumption and having high triglycerides, total or LDL cholesterol, low HDL cholesterol, high blood pressure or hypertension. Men and women were analysed separately. The majority of the CDAH sample is Caucasian, and therefore the analyses were not stratified by race. All statistical analyses were conducted with STATA software (version 10.1, 2009, Statacorp, College Station, TX, USA).

Results

In total, 2255 participants answered the takeaway food question and gave a fasting blood sample. Participants were excluded from the analysis if they had missing values for covariates included in the adjusted models (n=309) or were pregnant (n=50). This left 1896 participants for the analysis (22% of the original Australian Schools Health and Fitness Survey sample and 84% of those who completed the takeaway food question). One woman with an influential low glucose concentration (1.6 mmol/l) was excluded from the analysis of fasting glucose and HOMA.

The characteristics of the study sample are shown in Table 1. The CDAH sample had a higher percentage of participants who were married or living as married than the Australian population of 25–34-year olds (57% of men and 64% of women in the general population (Australian Bureau of Statistics, 2006)), and a higher percentage employed as professionals or managers (40% of men and 38% of women in the general population (Australian Bureau of Statistics, 2001)). The percentage classified as being overweight or obese (BMI 25 kg/m2) in the CDAH sample was very similar to the Australian population of similar age (58% of men and 35% of women (Australian Bureau of Statistics, 2004–2005)). Participants who did not attend the study clinics or were excluded from the analyses tended to be of lower socio-economic status (35% had a university qualification and 46% were employed as professionals or managers) than those included in the analyses. The percentage classified as being overweight (37%) or obese (16%) was similar to those included in the analyses.

Table 1 Socio-demographic, lifestyle and anthropometric characteristics of participants

The majority of participants ate takeaway food once a week or less (60.9% of men and 80.0% of women, Figure 1). Takeaway food was consumed twice a week or more by 39.1% of men and 20.0% of women.

Figure 1
figure1

Frequency of takeaway food consumption for men (n=914) and women (n=982) aged 26–36 years.

Women who ate takeaway at least twice a week had significantly higher mean fasting glucose concentrations and HOMA scores, and tended to have higher fasting insulin concentrations than those who ate takeaway once a week or less (Table 2). The results were adjusted for age, employment status, physical activity (leisure time physical activity) and sedentary behaviour (TV viewing). Additional adjustments for daily fruit, vegetable and fish consumption did not change the magnitude or the significance of the associations observed (data not shown). When waist circumference was added to the model, the differences between those who ate takeaway once a week or less and those who ate takeaway twice a week or more were reduced (mean fasting glucose 4.82 vs 4.87 mmol/l, respectively, P=0.101; HOMA 1.31 vs 1.37, respectively, P=0.214; fasting insulin 6.10 vs 6.32 mU/l, respectively, P=0.313). These differences were not explained by participants with diabetes. When those with self-reported diabetes and those with fasting glucose >6 mmol/l (18 women) were excluded from the analyses, the difference in glucose concentrations persisted (mean glucose 4.84 vs 4.90 mmol/l, P=0.052). The strength and the significance of the associations for fasting insulin and HOMA did not change. Men who ate takeaway twice a week or more also had higher fasting insulin concentrations and a higher HOMA score than those who ate takeaway less frequently, but the differences between the two groups were not statistically significant. Adjusting for waist circumference also attenuate the differences between men who ate takeaway once a week or less and those who ate takeaway twice a week or more (insulin 6.41 vs 6.39 mU/l, respectively, P=0.937; HOMA 1.47 vs 1.46, respectively, P=0.818). There were no significant differences between the two takeaway food groups for triglycerides, total cholesterol, LDL or HDL cholesterol, blood pressure or metabolic syndrome score for men or women. Including BMI in the model instead of waist circumference did not change the strength or the significance of the associations.

Table 2 Adjusted means (95% CI) for cardio-metabolic variables by takeaway food consumption

When we adjusted for smoking status, the difference in fasting insulin concentration for men was magnified (mean fasting insulin 6.09 mU/l takeaway 1 per week vs 6.65 mU/l takeaway 2 per week, P=0.036) owing to an unexpected negative association between smoking status and insulin. For women, adjusting for smoking status resulted in almost no change in the differences in glucose, insulin and HOMA.

After adjusting for confounders, the proportion of participants classified as having high triglycerides, total or LDL cholesterol, low HDL cholesterol, or high blood pressure or hypertension was not significantly different between the two takeaway food groups for men or for women (Table 3).

Table 3 Prevalence ratios of adverse lipids and blood pressure for frequency of takeaway food consumption

Discussion

In this sample of Australian adults, the majority of participants reported eating takeaway food once a week or less, which is consistent with other recent Australian studies (Thornton et al., 2009; Miura et al., 2011). Women who ate takeaway food twice a week or more had higher fasting glucose, insulin and HOMA insulin sensitivity scores than those who ate takeaway once a week or less. Adjustment for waist circumference or BMI attenuated these associations, which suggest that the effect of takeaway food consumption on these risk factors was mediated by obesity.

Although the differences between the two takeaway food groups for fasting glucose and HOMA were statistically significant for women, the differences were small. It is unclear whether the differences are clinically significant, but they may represent an early-impaired insulin sensitivity and foreshadow increased risk of cardiovascular disease and type 2 diabetes. Previous epidemiological studies have shown that insulin resistance and fasting insulin is positively associated with the risk of coronary heart disease (Folsom et al., 1997; Lawlor et al., 2007).

In this sample of young adults, women appeared to have a better metabolic profile than men, irrespective of takeaway food consumption. This is consistent with our previous analysis in this cohort that suggested that women were more likely than men to meet dietary recommendations for breads and cereals, vegetables, fruit and lean meat and alternatives (Smith et al., 2009). In this study, differences between women and men were most pronounced among those who ate takeaway food once a week or less, and our previous results showed that those who ate takeaway more frequently had poorer diet quality. Participants who ate takeaway infrequently were the reference group in the sex-specific analyses, and the relative effect of eating takeaway food on cardio-metabolic risk factors was therefore stronger for women than for men.

Our results that frequent takeaway food consumption was associated with insulin resistance for women, and less convincingly for men, is consistent with the findings from the CARDIA study (Pereira et al., 2005; Duffey et al., 2009). The CARDIA study did not report associations separately by sex. We found no association between takeaway food consumption and fasting lipid concentrations. In contrast, participants in the CARDIA study who ate fast food regularly had higher triglyceride concentrations and lower HDL cholesterol concentrations (Duffey et al., 2009). The CARDIA participants were older (38–50-years old at the 20-year follow-up) than those in the CDAH study, and this may explain the differences in the results. Cardiovascular disease risk factors are generally more common in older age groups, and older participants may have increased exposure through longer established takeaway consumption habits. It is possible that the effect of takeaway food consumption is shown early in glucose control, but a longer time frame is needed for the effect to be shown in lipids. In addition, the CARDIA study only examined associations with consumption of fast food from chain stores, whereas in the present study we also included other common takeaway food items.

We found no association between takeaway food consumption and blood pressure. Blood pressure increases with age, and in our sample of young adults there was a restricted range of values. This may have reduced our ability to detect the effect of takeaway food consumption on blood pressure. It is possible that associations between takeaway food consumption and blood pressure may be detected in later years when more variability in blood pressure exists and when takeaway food habits have been longer established.

There are several limitations of this study. Owing to the cross-sectional design, we were unable to determine the direction of a causal relationship between takeaway food consumption and cardio-metabolic risk in women. However, two longitudinal studies have shown that frequent fast food consumption at baseline is positively associated with cardio-metabolic risk factors, 13 and 15 years later (Pereira et al., 2005; Duffey et al., 2009). The large loss to follow-up and the proportion of participants excluded from the analyses because of missing data is a potential limitation of this study. Whilst we cannot discount the possibility of bias due to differential loss to follow-up, we have retained heterogeneity in the distribution of study factors, and this is an important factor with respect to external validity of analytical findings. High levels of takeaway food consumption were not very common in our sample, and we were unable to examine a dose-response relationship because of the small number of participants who were eating takeaway food three or more times per week. Our takeaway food question only asked about hot takeaway food and excluded cold items such as sandwiches, salads or other snack foods like doughnuts, muffins and pastries. In addition, we do not have data on the quality of the takeaway food consumed. Some participants might have chosen healthier items from the takeaway menus, and this would have reduced the strength of the associations observed. The nutrient composition of Chinese and Thai foods would also be different to that of foods purchased from the main fast food chains, such as hamburgers and french fries, in their composition of trans fatty acids, vitamins and antioxidants. Further studies that compare the effects on cardio-metabolic risk factors of items from fast food chains with other takeout options would be useful. We did not have a measure for energy intake. However, stratifying the analysis by sex and adjusting for age and physical activity would have partially accounted for energy intake.

Strengths of this study include its assessment of usual takeaway food consumption and the inclusion of popular takeaway food items such as Indian and Chinese foods, as well as food typically purchased from the main fast food chains. Other strengths include the large national sample and measures of a wide range of socio-demographic and lifestyle factors that we were able to include in our models to reduce possible confounding. The present study is one of the few studies to examine associations between takeaway food consumption and biochemical cardio-metabolic risk factors, and the only study to examine whether associations are mediated by obesity.

In conclusion, consuming takeaway food at least twice a week was associated with cardio-metabolic risk factors, particularly for women in this sample, and the results suggest that the effect was mediated by obesity.

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Acknowledgements

We gratefully acknowledge the contributions of the study's project manager, Marita Dalton, all other project staff and the study participants. This research was funded by grants from the National Health and Medical Research Council, the National Heart Foundation, the Tasmanian Community Fund and Veolia Environmental Services. We gratefully thank the study sponsors Sanitarium, ASICS and Target. SM was supported by a National Heart Foundation of Australia Postdoctoral Fellowship and SG was supported by a National Health and Medical Research Public Health Postdoctoral Fellowship.

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The funding bodies and sponsors had no input into the study design, collection, analysis and interpretation of data, the writing of the report or in the decision to submit the paper for publication.

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Contributors: KS performed the statistical analysis and drafted the manuscript, LB provided statistical support and critically revised the manuscript, SM provided nutritional advice and critically revised the manuscript, SG provided critical revision of the manuscript, TD was involved in the conceptualisation of the study and critically revised the manuscript and AV was involved in the conceptualisation of the study, acquired the data and critically revised the manuscript.

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Smith, K., Blizzard, L., McNaughton, S. et al. Takeaway food consumption and cardio-metabolic risk factors in young adults. Eur J Clin Nutr 66, 577–584 (2012). https://doi.org/10.1038/ejcn.2011.202

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Keywords

  • takeaway food
  • cardio-metabolic risk
  • abdominal obesity
  • insulin
  • lipids

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