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Keywords:

  • Cardiovascular risk factors;
  • children;
  • European cohort;
  • food consumption

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

What is already known about this subject

  • Few studies addressing the relationship between food consumption and cardiovascular disease or metabolic risk have been conducted in children. Previous findings have indicated greater metabolic risk in children with high intakes of solid hydrogenated fat and white bread, and low consumption of fruits, vegetables and dairy products.

What this study adds

  • In a large multinational sample of 2 to 9 years old children, high consumption of sweetened beverages and low intake of nuts and seeds, sweets, breakfast cereals, jam and honey and chocolate and nut-based spreads were directly associated with increased clustered cardiovascular disease risk. These findings add new evidence to the limited literature available in young populations on the role that diet may play on cardiovascular health.

Objective

To investigate food consumption in relation to clustered cardiovascular disease (CVD) risk.

Methods

Children (n = 5548, 51.6% boys) from eight European countries participated in the IDEFICS study baseline survey (2007–2008). Z-scores of individual CVD risk factors were summed to compute sex- and age-specific (2–<6 years/6–9 years) clustered CVD risk scores A (all components, except cardiorespiratory fitness) and B (all components). The association of clustered CVD risk and tertiles of food group consumption was examined.

Results

Odds ratio (OR) of having clustered CVD risk A increased in older children with higher consumption of chocolate and nut-based spreads (boys: OR = 0.46; 95% CI = 0.32–0.69; girls: OR = 0.60; 95% CI = 0.42–0.86), jam and honey (girls: OR = 0.45; 95% CI = 0.26–0.78) and sweets (boys: OR = 0.69; 95% CI = 0.48–0.98). OR of being at risk significantly increased with the highest consumption of soft drinks (younger boys) and manufactured juices (older girls). Concerning CVD risk score B, older boys and girls in the highest tertile of consumption of breakfast cereals were 0.41 (95% CI = 0.21–0.79) and 0.45 (95% CI = 0.22–0.93) times, respectively, less likely to be at risk than those in tertile 1.

Conclusions

High consumption of sugar-sweetened beverages and low intake of breakfast cereals, jam and honey, sweets and chocolate and nut-based spreads seem to adversely affect clustered CVD risk.


Abbreviations
BMI

body mass index

HDL-c

high-density lipoprotein cholesterol

TC

total cholesterol

TG

triglycerides

HOMA-IR

homeostatic assessment model

SBP

systolic blood pressure

DBP

diastolic blood pressure

CFR

cardiorespiratory fitness

VO2max

maximum oxygen uptake

CVD

cardiovascular disease

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

Increased risk of cardiovascular diseases (CVD) is characterized by a cluster of metabolic abnormalities including abdominal obesity, atherogenic dyslipemia, hypertension, insulin resistance and glucose intolerance [1]. During the last decades, clustering of CVD and metabolic risk factors have frequently been observed among children and adolescents [2]. In children, criteria still have to be established reflecting a universally accepted paediatric definition for metabolic syndrome, strongly depending on the age group assessed. This is of concern given that the onset of related risk factors occurring in early childhood may persist during adulthood [3], making therefore an early diagnosis essential.

CVD and metabolic risk factors in children have been related to lifestyle factors such as diet [4-6] and physical activity [7]. Most of the dietary studies carried out in adults and children have focused on intakes of nutrients, not on food consumption [5, 6, 8, 9]. Addressing potential associations between foods or food groups consumption might help to capture some of the complexity of nutrient-based analysis taking into consideration however, existing problems of food-based analysis [9]. An independent association between low fruit and vegetable consumption and high sweetened beverage consumption with rising prevalence of metabolic syndrome has been observed among adults [10]. In children, results have indicated greater metabolic risk with high intakes of solid hydrogenated fat and white bread, and low consumption of fruits, vegetables and dairy products [4]. Despite this, research in young age population groups is scarce. The aim of the present study was to examine the association between food consumption and clustered CVD risk factors in (pre)school children.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

Data used in this report were obtained from the baseline survey (2007–2008) of the IDEFICS (Identification and prevention of Dietary- and lifestyle-induced health EFfects in children and InfantS) study carried out in eight European countries: Italy, Estonia, Cyprus, Belgium, Sweden, Germany, Hungary and Spain. A total of 16 224 children aged 2–9 years were measured. More details about the study procedures have been previously published [11].

Only those subjects with complete data on gender, weight, height, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), glucose, insulin systolic blood pressure (SBP), and diet were included (n = 5548). No differences were observed between individuals with valid measurements and those excluded when considering mean body mass index (BMI). The study sample differed with respect to mean age, height and weight from the total IDEFICS sample since they were older, taller and heavier (P < 0.05). The study protocol was approved by the ethics committee at each study centre and written informed consent signed by the parents was obtained from each participating child.

Socioeconomic status

The International Standard Classification of Education (ISCED) [12] was used as indicator for socioeconomic status (SES). The maximum ISCED level of both parents was considered.

Food consumption

The food frequency section of the Children's Eating Habits Questionnaire (CEHQ-FFQ) was completed by the parents, querying on the number of times the child had consumed the food item at home or at other people's homes during a typical week over the previous month. The CEHQ-FFQ included 43 food groups, which were clustered in 25 food groups according to their nutritional values and used in subsequent analysis: vegetables; fried potatoes; fruit; manufactured fruit juices; soft drinks; breakfast cereals; milk; dairy products (yoghurt, fermented milk beverages …); cold cuts (ham, sausages, salami, mortadella …); cheese; jam and honey; chocolate and nut-based spreads (Nutella, peanut butter …); butter and margarine; low-fat spreads (light butter, light margarine …); ketchup; refined cereals (white bread, pasta and rice); pizza; nuts and seeds (almonds, walnuts, sunflower seeds, raisins …); snacks (popcorn, crisps, chips, pancakes …); and sweets (candies, chocolate, cakes, desserts, ice creams …). Food groups included in the analysis were selected based on their relation to health-related practices and obesity prevalence [13-15]. Responses included eight frequency categories of consumption: ‘never/less than once a week’, ‘1–3 times a week’, ‘4–6 times a week’, ‘1 time per day’, ‘2 times per day’, ‘3 times per day’, ‘4 or more times per day’ and ‘I have no idea’. Frequencies were converted into times per week ranging from 0 to 30. Previous findings suggested acceptable reproducibility of the CEHQ-FFQ [16] and found a positive correlation between milk consumption frequencies and potassium and calcium urinary excretion ratios [17].

The number of meals per week consumed at home or at other people's homes was also reported. For every meal, parents had to choose one of the following frequencies: ‘daily’, ‘only at weekdays’, ‘only at weekends’, ‘several times per week’ and ‘on fewer occasions’.

Physical examinations

Weight was measured in light underwear with an electronic scale (TANITA BC 420 SMA, Tanita Europe GmbH, Sindelfingen, Germany) and height was measured without shoes using a stadiometer (Seca 225, Birmingham, UK). BMI was calculated as weight (kg) divided by squared height (m2). Skinfolds thicknesses were measured twice with a calliper (Holtain Ltd, Croswell, UK) at the triceps and subscapular sites and the mean of the two measurements was taken. Blood pressure was measured with an electronic sphygmomanometer (Welch Allyn 4200B-E2, Welch Allyn Inc., Skaneateles Falls, NY, USA) in the right arm with the child in a sitting position. Two measurements were taken at 2 min intervals and differences of 5% magnitude lead to take a third measurement. Means of replicate measurements were used in all analyses. All physical examinations were taken by trained fieldworkers.

Physical activity and sedentary behaviours

Parents were asked about the sum of hours that their children spent playing outdoors (weekday and weekend days) in addition to weekly participation in sport club activities. This was done via an outdoor playtime questionnaire [18], which had previously been significantly correlated with objective measures of physical activity (PA) (Spearman r = 0.20, P = 0.003). Children's weekly participation in sport club activities significantly correlated (Spearman r = 0.23, P < 0.001) with children's daily time spent in moderate to vigorous activity as measured by accelerometry within the IDEFICS study (unpublished results).

The average hours per week of TV/video/DVD viewing was obtained by asking the parents ‘How long does your child usually watch TV/video/DVD per day?’ Responses were split into weekdays and weekend days and included five categories: ‘not at all’, ‘<30 minutes per day’, ‘<1 hour per day’, ‘1–2 hours per day’, ‘2–3 hours per day’ and ‘>3 hours per day’. The total number of hours per week of TV/video/DVD viewing was calculated.

Physical fitness

Cardiorespiratory fitness (CRF; mL kg−1 min−1) was predicted by means of the Léger et al. [19] formula using the maximum speed that the child reached during the 20-m shuttle run test. Participants were required to run between two lines 20 m apart following beep signals in gradual increase of speed. When the participant stopped due to fatigue or did not reach the line with the audio signal on two consecutive occasions, the test finished. The final score was computed as the number of stages completed and was used to calculate the maximum oxygen uptake (VO2max) [19].

Biological samples

A detailed description of the blood sampling procedures is published elsewhere [20]. Blood samples were obtained after an overnight fast. Blood glucose, TC, HDL-c and TG were assessed at each study centre and serum insulin concentrations were determined in a central laboratory. Insulin resistance as defined by the homeostasis model assessment (HOMA-IR) [21] was calculated via a standard formula from fasting glucose and plasma insulin: HOMA-IR = [insulin (μUI mL−1) × glucose (mg dL−1)]/405.

Cardiovascular disease risk score

A continuous score of clustered CVD risk factors was computed using the following variables according to Andersen et al. [7]: SBP, TG, ratio TC/HDL-c, HOMA and sum of two skinfolds (tricipital and subscapular). Since the 20-m shuttle run test was only performed in children older than 6 years, a second CVD risk score was only obtained for older children containing the CRF variable. Gender- and age group-specific (2–<6 years/6–9 years) z-scores were calculated for each risk factor variable. All individual z-scores were summed to create two clustered CVD scores: CVD risk score A (without CRF) and CVD risk score B (with CRF), computed only in older children. CRF was multiplied by −1 to indicate higher CVD risk with increasing value. The lower the score the better the overall CVD risk factor profile.

Statistical analysis

The Predictive Analytics SoftWare (version 18; SPSS Inc., Chicago, IL, USA) was used to perform the analyses. Statistical significance was set at P < 0.05. Frequencies of food group consumption (times per week) were converted into age-, gender- and study centre-specific tertiles. Food groups with high proportion of non-consumers (>33% non-consumers) were treated differently, i.e. participants with zero consumption were considered as one category. The remaining participants were split into two halves of consumption according to their medians. This resulted in unbalanced tertiles in terms of number of participants. That approach was applied for fried potatoes (only for analysis with CVD risk score A), soft drinks (only for analysis with CVD risk score A), jam and honey, low-fat spreads, and chocolate and nut-based spreads (only for analysis with CVD risk score B). The number of non-consumers for pizza, nuts and fried potatoes (only for analysis with CVD risk score B) food groups were >66%. For these food groups, comparisons were based on consumers and non-consumers. The association between clustered CVD risk and food group consumption was assessed by binary logistic regression. Children above 1 standard deviation (SD) for clustered risk scores were identified as being at risk of CVD [7]. Model 1 was adjusted for SES, study centre and the number of meals per week consumed at home or at other people's homes. Model 2 included the covariates of model 1 plus physical activity.

Since only older children had valid data on CRF (n = 1332), additional gender- and study centre-specific tertiles were computed for food groups in those analysis conducted with CVD risk score B. The same approach described above was applied for low consumed food groups.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

Descriptive characteristics of participants by age group and gender are shown in Table 1. The risk of having clustered risk factors (dichotomous z-score above 1 SD) according to tertiles of food group consumption is presented separately by age and gender group for CVD risk score A and B.

Table 1. Values for CVD risk-related factors and anthropometric measures of study participants by age group and gender
 2–<6 years old6–9 years old
Boys (n = 1103)Girls (n = 982)Boys (n = 1760)Girls (n = 1703)
  1. *BMI categories according to Cole et al. [34].

  2. BMI, body mass index; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; DBP, diastolic blood pressure; HDL-c, high-density lipoprotein cholesterol; HOMA, homeostasis model assessment; SBP, systolic blood pressure; TG, triglycerides. All data expressed as mean (SD).

Age (years)4.4 (0.9)4.4 (0.9)7.5 (0.8)7.5 (0.8)
Height (cm)107.8 (7.6)106.5 (7.7)127.9 (7.4)126.6 (7.3)
Weight (kg)18.6 (3.7)18.0 (3.6)27.7 (6.5)27.2 (6.3)
BMI (kg m−2)15.9 (1.8)15.8 (1.8)16.8 (2.8)16.8 (2.8)
Underweight (%)*165 (15)130 (13.2)164 (9.3)152 (8.9)
Normal weight (%)804 (72.9)695 (70.8)1217 (69.1)1132 (66.5)
Overweight (%)92 (8.3)109 (11.1)244 (13.9)292 (17.1)
Obese (%)42 (3.8)48 (4.9)135 (7.7)127 (7.5)
Sum of two skinfolds (mm)15.6 (4.7)17.7 (5.4)17.9 (8.6)21.2 (9.2)
DBP (mm Hg)61.0 (6.2)62.0 (6.1)63.3 (6.6)64.1 (6.3)
SBP (mm Hg)96.7 (8.3)96.7 (8.1)102.8 (8.5)102.1 (8.6)
TG (mg/dL)40.9 (22.6)42.1 (23.7)40.5 (21.7)43.1 (25.2)
Total cholesterol (mg dL−1)155.7 (29.3)157.9 (30.4)159.5 (29.8)164.7 (32.2)
HDL-c (mg dL−1)49.1 (13.5)46.6 (13.6)54.9 (14.8)53.8 (14.4)
Glucose (mg dL−1)82.8 (9.2)81.1 (8.9)87.8 (9.0)85.5 (8.7)
Insulin (μU mL−1)3.1 (2.4)3.5 (2.8)4.7 (3.1)5.4 (3.6)
HOMA score0.7 (0.6)0.7 (0.6)1.0 (0.7)1.2 (0.8)
CRF (mL kg−1 min−1)48.0 (3.0)47.0 (2.3)
CVD risk score A−1.24 (2.53)−0.44 (2.60)0.12 (2.89)0.93 (3.06)
CVD risk score B−0.001 (2.77)0.63 (2.80)

Data regarding CVD risk score A are presented in Table 2 for boys and Table 3 for girls. Younger boys consuming nuts and seeds were 0.62 (95% CI = 0.40–0.95) times less likely of being at CVD risk than those with no consumption. Considering soft drinks, the odds ratio (OR) of having clustered CVD risk increased for younger boys in tertile 3 (OR = 1.57; 95% CI = 1.00–2.46) compared with those in tertile 1. In addition, the odds of having CVD risk decreased for children in the highest tertile of chocolate and nut-based spreads consumption for both older boys (OR = 0.46; 95% CI = 0.32–0.69) and girls (OR = 0.60; 95% CI = 0.42–0.86) compared with those with the lowest consumption. Older boys with the highest consumption of sweets showed lower likelihood of having clustered risk (OR = 0.69; 95% CI = 0.48–0.98) than boys in tertile 1. Older girls in tertile 3 of jam and honey were 0.45 (95% CI = 0.26–0.78) times less likely to be at CVD risk than those in tertile 1. The OR for being at risk of clustered CVD increased for older girls with the highest consumption of manufactured juices (OR = 1.45; 95% CI = 1.01–2.10) compared with those with the lowest consumption.

Table 2. Age-specific odd ratios (OR) for CVD risk score A for tertiles of food groups in boys
 2–<6 years old6–9 years old
Model 1Model 2Model 1Model 2
OR95% CIOR95% CIOR95% CIOR95% CI
  1. *These food groups have been split into consumers and non-consumers (reference group) due to the high proportion of non-consumers (>66%) in these groups.

  2. Model 1: adjusted for SES, study centre and number of meals per week consumed at home or at other people's homes.

  3. †Model 2: adjusted for socioeconomic status (SES), study centre, number of meals per week consumed at home or at other people's homes and total physical activity performed during the week.

  4. ‡CI, confidence interval; CVD, cardiovascular disease.

Reference for all food groups: tertile 1
Vegetables (raw and cooked)
Tertile 21.080.70,1.671.140.72,1.790.810.58,1.140.810.57,1.15
Tertile 31.040.66,1.651.110.69,1.780.840.60,1.200.880.61,1.26
Fried potatoes
Tertile 20.750.50,1.110.740.49,1.130.900.67,1.210.850.63,1.16
Tertile 30.880.33,2.371.040.38,2.850.550.21,1.450.540.20,1.44
Fruit
Tertile 20.720.45,1.160.720.44,1.180.890.61,1.290.910.62,1.33
Tertile 30.670.41,1.080.690.42,1.141.340.95,1.870.940.94,1.93
Manufactured juices
Tertile 20.910.58,1.420.880.56,1.410.900.62,0.290.930.64,1.35
Tertile 31.090.68,1.741.100.67,1.781.040.62,1.291.030.71,1.49
Soft drinks
Tertile 20.680.38,1.210.700.38,1.280.910.62,1.320.870.59,1.29
Tertile 31.410.92,2.171.571.00,2.461.050.74,1.470.980.69,1.40
Breakfast cereals
Tertile 21.320.83,2.081.480.92,2.371.100.78,1.571.090.76,1.56
Tertile 30.880.55,1.400.930.57,1.520.770.53,1.120.730.50,1.07
Milk
Tertile 20.850.53,1.350.850.53,1.380.890.62,1.280.830.57,1.20
Tertile 31.020.66,1.581.010.64,1.590.830.59,1.170.790.56,1.13
Dairy products
Tertile 21.250.79,1.961.240.77,1.980.960.67,1.380.990.68,1.44
Tertile 30.890.57,1.380.960.61,1.521.010.73,1.411.050.75,1.47
Cold cuts
Tertile 20.740.47,1.160.680.42,1.100.970.68,1.390.960.67,1.38
Tertile 30.840.53,1.330.880.55,1.401.060.74,1.521.050.73,1.52
Cheese
Tertile 21.230.78,1.931.330.83,2.131.150.81,1.621.120.78,1.59
Tertile 31.010.62,1.621.120.68,1.831.320.92,1.891.320.91,1.91
Jam and honey
Tertile 21.080.68,1.701.070.67,1.710.690.48,0.990.690.48,0.99
Tertile 30.880.50,1.530.920.51,1.651.120.74,1.701.090.71,1.68
Chocolate and nut-based spreads
Tertile 20.950.56,1.620.950.55,1.650.840.57,1.240.760.51,1.12
Tertile 30.990.65,1.510.980.63,1.520.530.36,0.760.460.32,0.69
Butter and margarine
Tertile 20.920.53,1.600.860.48,1.521.550.99,2.421.560.99,2.46
Tertile 30.700.41,1.220.740.42,1.291.100.71,1.711.100.70,1.72
Low-fat spreads
Tertile 21.100.55,2.201.140.55,2.330.790.46,1.340.730.42,1.26
Tertile 31.440.86,2.401.470.87,2.510.910.60,1.390.900.59,1.39
Ketchup
Tertile 20.890.54,1.460.980.59,1.641.210.81,1.811.240.82,1.88
Tertile 31.260.75,2.111.340.78,2.311.310.87,1.981.300.86,1.98
Refined cereals (white bread, pasta, rice)
Tertile 21.280.82,2.001.300.82,2.071.561.10,2.221.481.04,2.12
Tertile 31.020.64,1.621.140.71,1.821.020.71,1.480.920.63,1.35
Pizza*
Tertile 2
Tertile 31.490.99,2.251.380.89,2.121.180.86,1.611.160.85,1.60
Nuts and seeds*
Tertile 2
Tertile 30.670.44,1.010.620.40,0.950.880.65,1.200.880.64,1.21
Snacks
Tertile 20.880.47,1.660.800.42,1.530.990.70,1.411.010.71,1.44
Tertile 30.870.58,1.290.880.58,1.331.050.73,1.501.020.71,1.48
Sweets (candies, chocolate, cakes, desserts, ice creams)
Tertile 20.960.60,1.520.930.58,1.490.760.54,1.070.720.50,1.03
Tertile 31.440.92,2.251.420.90,2.240.740.52,1.050.690.48,0.98
Table 3. Age-specific odd ratios (OR) for CVD risk score A for tertiles of food groups in girls
 2–<6 years old6–9 years old
Model 1Model 2Model 1Model 2
OR95% CIOR95% CIOR95% CIOR95% CI
  1. *These food groups have been split into consumers and non-consumers (reference group) due to the high proportion of non-consumers (>66%) in these groups.

  2. †Model 1: adjusted for socioeconomic status (SES), study centre and number of meals per week consumed at home or at other people's homes.

  3. ‡Model 2: adjusted for SES, study centre, number of meals per week consumed at home or at other people's homes and total physical activity performed during the week.

  4. CI, confidence interval; CVD, cardiovascular disease.

Reference for all food groups: tertile 1
Vegetables (raw and cooked)
Tertile 21.130.70,1.811.150.70,1.901.020.71,1.461.040.71,1.51
Tertile 30.790.49,1.300.870.52,1.440.800.57,1.130.840.58,1.20
Fried potatoes
Tertile 21.230.81,1.861.010.65,1.581.090.81,1.491.000.73,1.38
Tertile 30.210.03,1.620.250.03,2.070.870.35,2.170.800.29,2.20
Fruit
Tertile 20.720.45,1.160.720.44,1.180.890.61,1.290.910.62,1.33
Tertile 30.670.41,1.080.690.42,1.141.340.95,1.870.940.94,1.93
Manufactured juices
Tertile 21.180.72,1.941.160.70,1.940.910.61,1.370.900.59,1.37
Tertile 31.000.62,1.600.840.51,1.391.481.04,2.111.451.01,2.10
Soft drinks
Tertile 21.761.06,2.931.711.01,2.900.890.60,1.310.910.61,1.37
Tertile 31.260.76,2.101.100.64,1.911.050.73,1.501.060.72,1.53
Breakfast cereals
Tertile 21.490.91,2.451.560.93,2.601.150.80,1.641.090.75,1.58
Tertile 31.060.65,1.751.070.63,1.800.860.60,1.240.830.56,1.21
Milk
Tertile 20.950.59,1.541.010.61,1.670.860.61,1.210.870.61,1.24
Tertile 30.970.59,1.591.000.60,1.680.850.58,1.240.840.56,1.24
Dairy products
Tertile 20.650.39,1.100.600.35,1.021.130.79,1.631.110.76,1.61
Tertile 31.050.67,1.640.890.56,1.431.190.84,1.681.200.84,1.73
Cold cuts
Tertile 20.550.34,0.910.540.32,0.901.180.82,1.701.180.81,1.72
Tertile 30.860.54,1.380.780.47,1.281.130.78,1.631.060.73,1.56
Cheese
Tertile 20.730.46,1.160.750.46,1.220.960.67,1.390.970.66,1.41
Tertile 30.720.44,1.190.810.48,1.371.000.70,1.431.030.71,1.49
Jam and honey
Tertile 20.780.48,1.260.780.47,1.301.110.78,1.561.110.78,1.58
Tertile 30.770.43,1.370.880.49,1.590.430.25,0.740.450.26,0.78
Chocolate and nut-based spreads
Tertile 20.750.44,1.250.670.39,1.150.770.50,1.180.790.50,1.24
Tertile 30.940.58,1.520.910.55,1.500.650.46,0.910.600.42,0.86
Butter and margarine
Tertile 20.630.35,1.140.600.33,1.110.790.51,1.210.830.53,1.30
Tertile 30.760.42,1.380.720.39,1.330.650.42,1.010.670.43,1.05
Low-fat spreads
Tertile 20.430.17,1.070.390.14,1.041.651.00,2.721.951.16,3.29
Tertile 31.260.76,2.111.240.73,2.091.310.86,2.001.380.89,2.15
Ketchup
Tertile 21.190.72,1.971.060.63,1.780.900.62,1.320.810.54,1.21
Tertile 31.630.93,2.861.610.90,2.891.250.82,1.911.020.65,1.59
Refined cereals (white bread, pasta, rice)
Tertile 21.040.64,1.691.100.66,1.830.830.59,1.180.820.57,1.19
Tertile 31.340.82,2.201.360.81,2.290.790.55,1.120.790.55,1.14
Pizza*
Tertile 2
Tertile 30.780.47,1.290.720.42,1.241.070.77,1.470.940.66,1.32
Nuts and seeds*
Tertile 2
Tertile 30.900.59,1.350.940.61,1.440.950.69,1.290.950.68,1.32
Snacks
Tertile 20.950.57,1.551.030.61,1.711.010.68,1.490.900.60,1.36
Tertile 30.920.56,1.520.930.55,1.580.970.69,1.380.840.58,1.21
Sweets (candies, chocolate, cakes, desserts, ice creams)
Tertile 20.940.59,1.490.930.57,1.500.980.69,1.410.910.63,1.32
Tertile 30.680.42,1.100.660.40,1.101.130.79,1.601.040.72,1.50

Table  presents data on CVD risk score B. Both older boys and girls in the highest tertile of consumption of breakfast cereals were 0.41 (95% CI = 0.21–0.79) and 0.45 (95% CI = 0.22–0.93) times, respectively, less likely to be at CVD risk than their peers in tertile 1. Additionally, the odds of having clustered CVD risk decreased for those boys with the highest consumption of chocolate and nut spreads (OR = 0.20; 95% CI = 0.07–0.56) and sweets (OR = 0.52; 95% CI = 0.27–0.99) when compared with boys in tertile 1.

Table 4. Gender-specific odd ratios for CVD risk score B for tertiles of food groups
 BoysGirls
Model 1Model 2Model 1Model 2
OR95% CIOR95% CIOR95% CIOR95% CI
  1. *These food groups have been split into consumers and non-consumers (reference group) due to the high proportion of non-consumers (>66%) in these groups.

  2. †Model 1: adjusted for socioeconomic status (SES), study centre and number of meals per week consumed at home or at other people's homes.

  3. ‡Model 2: adjusted for SES, study centre, number of meals per week consumed at home or at other people's homes and total physical activity performed during the week.

  4. CI, confidence interval; CVD, cardiovascular disease; NA, not available.

Reference for all food groups: tertile 1
Vegetables (raw and cooked)
Tertile 20.570.32,1.030.640.35,1.161.030.57,1.841.040.56,1.95
Tertile 30.500.26,0.940.540.28,1.020.800.44,1.440.890.47,1.68
Fried potatoes*
Tertile 2
Tertile 31.340.80,2.241.220.72,2.081.320.81,2.151.150.67,1.97
Fruit
Tertile 20.650.35,1.180.590.32,1.111.040.55,1.941.100.57,2.13
Tertile 30.630.34,1.170.590.31,1.111.230.69,2.191.290.69,2.40
Manufactured juices
Tertile 20.900.46,1.780.940.47,1.880.740.40,1.370.730.38,1.41
Tertile 30.940.53,1.680.900.50,1.611.310.72,2.361.180.62,2.24
Soft drinks
Tertile 21.080.52,2.261.060.49,2.270.750.37,1.520.720.33,1.58
Tertile 31.290.73,2.281.160.64,2.091.050.54,2.031.020.49,2.13
Breakfast cereals
Tertile 20.570.32,1.030.550.29,1.010.980.56,1.691.100.61,1.96
Tertile 30.410.22,0.790.410.21,0.790.440.23,0.850.450.22,0.93
Milk
Tertile 20.630.30,1.310.530.24,1.130.850.49,1.460.970.54,1.73
Tertile 30.990.57,1.711.050.60,1.850.830.43,1.610.790.38,1.64
Dairy products
Tertile 21.420.77,2.611.450.77,2.740.530.29,0.990.540.28,1.04
Tertile 31.010.55,1.841.050.57,1.950.980.56,1.731.000.54,1.86
Cold cuts
Tertile 21.110.60,2.101.190.63,2.220.970.55,1.741.100.59,2.04
Tertile 31.200.64,2.251.210.63,2.290.950.53,1.721.100.58,2.08
Cheese
Tertile 20.760.41,1.390.730.39,1.360.880.49,1.590.870.47,1.64
Tertile 31.040.57,1.901.130.61,2.100.810.46,1.430.810.44,1.50
Jam and honey
Tertile 20.440.22,0.900.480.23,0.981.490.81,2.761.550.81,2.97
Tertile 30.890.43,1.861.010.47,2.180.970.44,2.131.150.49,2.67
Chocolate and nut-based spreads
Tertile 20.730.40,1.300.630.34,1.150.960.53,1.731.170.62,2.20
Tertile 30.260.10,0.690.200.07,0.560.590.28,1.260.530.23,1.20
Butter and margarine
Tertile 31.000.48,2.091.000.47,2.150.650.32,1.300.820.39,1.76
Tertile 30.700.33,1.500.700.32,1.540.410.20,0.850.570.26,1.24
Low-fat spreads
Tertile 21.210.59,2.501.170.55,2.472.631.29,5.363.231.51,6.92
Tertile 30.790.35,1.770.870.38,1.980.330.63,2.801.300.57,2.99
Ketchup
Tertile 30.260.10,0.690.200.07,0.560.590.28,1.260.530.23,1.20
Tertile 30.260.10,0.690.200.07,0.560.590.28,1.260.530.23,1.20
Refined cereals (white bread, pasta, rice)
Tertile 21.700.94,3.061.700.94,3.060.640.35,1.170.640.35,1.17
Tertile 30.810.41,1.620.810.41,1.620.670.34,1.300.670.34,1.30
Pizza*
Tertile 2
Tertile 31.130.62,2.071.130.62,2.081.100.61,1.970.880.46,1.71
Nuts and seeds*
Tertile 2
Tertile 30.800.47,1.370.790.45,1.370.840.51,1.400.800.45,1.40
Snacks
Tertile 21.080.58,2.011.180.63,2.241.400.76,2.581.260.65,2.45
Tertile 30.940.51,1.750.950.51,1.801.871.05,3.341.620.87,3.02
Sweets (candies, chocolate, cakes, desserts, ice creams)
Tertile 20.620.35,1.120.610.33,1.111.670.92,3.001.520.81,2.85
Tertile 30.600.32,1.120.520.27,0.991.230.66,2.290.980.49,1.93

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

This study investigated the association between food consumption and clustering of CVD risk factors. Examination of clusters of CVD risk factors has been shown to be a more informative approach than single-risk factors in children [22]. Additionally, a composite score could compensate in part for fluctuations in the single-risk factors [7]. On the other hand, single cut-off points cannot be used to define metabolic abnormalities as they vary with age, gender and pubertal age [23]. It is noteworthy, however, that CVD risk factors have also been observed at early ages, mainly among obese children [21, 22]. For that reason, specific CVD risk scores were computed by age group and gender.

Observed significant associations between CVD risk score A and tertiles of food group consumption persisted in CVD risk score B for boys > 6 years but not in girls. Furthermore, older children with the highest consumption of breakfast cereals were less likely of having clustered CVD risk B compared with those in tertile 1, whereas no significant differences were observed for CVD risk score A. These differences could be attributed to the effect of the CRF variable. Indeed, it was found that CRF was positively associated with bread and cereals consumption in European adolescent boys [24].

Our findings indicated that higher consumption of soft drinks in younger boys and manufactured juices in older girls were associated with higher risk of clustered CVD risk. In a study [6] performed in Mexican schoolchildren, foods rich in fat and sugar were identified to be related more to CVD risk factors. Ventura et al. [25] suggested that at ages 5, 7 and 9 years, those with higher metabolic risk had the highest daily sweetened beverage consumption compared with others being at lower risk. Increased sugar-sweetened beverages consumption have been associated with increased HOMA-IR, SBP, waist circumference and decreased HDL-c concentrations in US adolescents [26] and with increased glucose concentrations in Mexican children [6].

Higher consumption of breakfast cereals decreased the OR of having clustered CVD risk B in both older boys and girls compared with those children with the lowest consumption. These beneficial effects may be explained partly by the presence of wholegrain breakfast cereals, which have been inversely associated to CVD-specific mortality in adults [27]. Unexpectedly, older boys and girls with higher consumption of several sugar-rich products, i.e. chocolate and nut-based spreads, jam and honey (girls only), and sweets (boys only) were at lower risk of having clustered CVD compared with those with the lowest consumption. Although these products have more frequently been related to increased CVD or metabolic risk in children [6], one study performed in the United States [28] observed that candy consumption did not negatively affect CVD risk factors in both children and adolescents. Additionally, these products are typically consumed at breakfast. A recent study carried out among Italian adults suggested that frequent consumption of breakfast foods such as breakfast cereals, honey, sugar and jam positively affected their CVD risk profile [29]. On the other hand, results observed among younger children are in agreement with a recent study, which associated nut consumption with a decreased prevalence of CVD risk factors, type 2 diabetes and metabolic syndrome in adults [30].

The present study has several limitations. The cross-sectional design does not allow us to conclude that dietary intakes of certain foods causally contributed to a greater CVD risk. Another limiting factor is dietary information has been obtained by proxy-reported food frequency questionnaire, which is subject to error in the total number of foods usually consumed [31]. However, the CEHQ-FFQ has previously been shown to give reproducible estimates of the frequency of food group consumption in European children [16, 17]. Obtained data from FFQs has been considered to be appropriate to explore patterns of intake and relationships with CVD risk factors [32]. The large number of non-consumers in each food group might also have influenced observed relationships. Additionally, the attenuation of effect estimates due to misreporting cannot be precluded; however, the prevalence of misreporting in this sample of European children is rather low compared with others [33].

Children with CVD risk score A or B above 1 SD were considered to be at risk. As a result, individuals not being at risk could have been classified as being at risk. Nevertheless, that misclassification would have resulted in an underestimation of the true association between food groups consumption and clustered CVD risk [7]. It is important to remark that the sample included in this study differs from the whole study population in terms of mean age, height and weight, so our conclusions can neither be generalized nor extrapolated to the whole IDEFICS sample.

The sample size was large enough to detect associations between food intake and the clustering of CVD risk factors. To our knowledge, this is the largest sample of children recruited from several European countries in which the relationship between CVD risk factors and food intake has been assessed. Furthermore, all procedures were standardized across countries.

In conclusion, our findings suggest that children being more likely to be at greater CVD risk had higher consumption of sugar-sweetened beverages (younger boys and older girls) and lower consumption of nuts and seeds (younger boys), sweets (older boys), jam and honey (older girls), chocolate and nut-based spreads and breakfast cereals (older boys and girls). These results can help to develop strategies aimed at promoting healthy lifestyles from very early in life by means of encouraging children to have healthier eating patterns and to engage in physical activity regularly. More studies, preferably with a longitudinal design, are needed to explore the relationship between CVD risk factors and food consumption during childhood.

Conflicts of Interest Statement

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

The authors declare no conflict of interest.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References

This work was done as part of the IDEFICS study and was published on behalf of its European Consortium (http://www.idefics.eu). We gratefully acknowledge the financial support of the European Community within the Sixth RTD Framework Programme Contract no. 016181 (FOOD). The information in this document reflects the author's view and is provided as is. SB-S was funded by a grant from the Aragón's Regional Government (Diputación General de Aragón, DGA).

The authors’ responsibilities were as follows: LAM, VK, SDH, SM, DM, AS, MT and TV planned and directed the study; SB-S, CB and JP conducted research; SB-S wrote the manuscript and performed the statistical analyses; TM, LAM, CB and JP participated in data interpretation. SB-S, TM, CB, JP, LAM, VK, SDH, SM, DM, AS, MT and TV critically reviewed the manuscript.

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conflicts of Interest Statement
  8. Acknowledgements
  9. References
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