European Journal of Pediatrics

, Volume 171, Issue 9, pp 1373–1382

Correlation of blood pressure, obesity, and adherence to the Mediterranean diet with indices of arterial stiffness in children

Authors

    • 2nd Medical DepartmentVenizeleion General Hospital
  • Evaggelia Stefanaki
    • Department of PediatricsVenizeleion General Hospital
  • Sofia Stefanaki
    • Department of PediatricsVenizeleion General Hospital
  • Evaggelos Thalassinos
    • 2nd Medical DepartmentVenizeleion General Hospital
  • Melina Kavousanaki
    • 2nd Medical DepartmentVenizeleion General Hospital
  • Danai Lydaki
    • University of Athens, Medical School
Original Article

DOI: 10.1007/s00431-012-1735-3

Cite this article as:
Lydakis, C., Stefanaki, E., Stefanaki, S. et al. Eur J Pediatr (2012) 171: 1373. doi:10.1007/s00431-012-1735-3

Abstract

The aim of the study was to assess the hypothesis that obesity, blood pressure (BP), and dietary habits (adherence to the Mediterranean diet) are related to indices of arterial stiffness (AS) in childhood. Two hundred and seventy-seven children aged 12 years were measured with the R6.5 Pulsecor® monitor, which performs measurements using an upper arm BP cuff held at above systolic pressure for a short time. The augmentation index (AI) in the brachial artery, the peripheral pulse pressure to central pulse pressure (PPP/CPP) ratio, and the reflected wave transit time to height ratio were used as indices of AS. The degree of adherence to the Mediterranean diet was assessed by the KIDMED index which includes 16 questions on specific dietary habits. Forty-three percent of the children were overweight and obese. Overweight and obese children had significantly lower PPP/CPP and KIDMED score in comparison to children with normal body mass index (BMI). In multivariate regression models, indices of AS were related to mean peripheral BP, heart rate, and height, while BMI had an independent correlation to PPP/CPP. The KIDMED index also had a negative correlation with AI independently of obesity. Conclusion: Obesity and adherence to the Mediterranean diet patterns are factors related independently to indices of AS even in 12-year-old children.

Keywords

Arterial functionArterial stiffnessChildhood obesityMediterranean dietHypertension

Introduction

Obesity in both children and adults is a global epidemic. In the USA, the prevalence of overweight/obese children/adolescents of 6–19 years old was 31 % according to the National Health and Nutritional Examination Survey data from 1992 to 2002 [13]. The International Obesity Task Force in 2003 reported that, in Greece, the prevalence of overweight children of 7–11 years was 31–33 % [18]. A more recent study in Greece has shown that the overweight and obesity prevalence among 10- to 12-year-old children was as high as 40 % [10]. There is a substantial line of evidence that obesity along with other risk factors like hypertension, high LDL, low HDL are predisposing conditions for increased arterial stiffness (AS) [12]. AS depends on the structural arterial properties, the smooth muscle cell tone, and the distending arterial pressure. It has been shown that, in adults, the pulse wave velocity (PWV) (which is a measure of AS) is an independent prognostic indicator for cardiovascular events [36].

Studies in children have shown that the appearance of fatty streaks in the arterial intima starts from childhood and is related to obesity [18] and with other components of the metabolic syndrome. This atherosclerotic procedure persists through adulthood. These observations point to the concept that the traditional risk factors exert their action for many decades before the development of overt cardiovascular disease. AS has been reported to be higher in obese children compared to those with normal weight [2, 11] in some studies. Hypertension—which often accompanies childhood obesity—leads to increased collagen synthesis and hyperplasia of smooth muscle cells and increased AS in young adults [34]. On the other hand, there have been occasional studies in the literature that have not reported the association of body mass index (BMI) with PWV in children [24, 28].

In the 1960s, the Seven Countries Study showed that cardiovascular mortality (especially coronary deaths) in the USA and Northern Europe greatly exceeded those in Southern Europe, even when controlled for age, cholesterol, blood pressure (BP), smoking, physical activity, and weight [14]. This was attributed to a great extent to the Mediterranean diet pattern. More recent data confirm the relationship between adherence to the Mediterranean diet and survival in a Greek population [32]. Nevertheless, a very high childhood obesity prevalence accompanied by low adherence rates to the Mediterranean diet coupled by low physical activity levels has been recently reported in Greece [4, 10].

The research hypothesis in the present study is that there is a correlation of BP, obesity, and adherence to the Mediterranean diet with indices of AS in a cohort of Greek children. Greece is one of the “low-cardiovascular-risk” countries of Europe according to the European Society of Hypertension in the SCORE chart [9] despite the fact that the prevalence of childhood obesity has been reported as one of the highest worldwide.

Methods and subjects

Three hundred and eighty-five children attending the first grade from three high schools in the urban area of Heraklion, island of Crete, in southern Greece were asked to enter the study. The participating schools were randomly selected among 25 high schools in Heraklion (110,000 inhabitants). Consent forms and informative leaflets about the procedures were given to the parents of the students. All subjects of the study gave their consent prior to the study. Exclusion criteria were unwillingness to give consent, subjects with treated cardiac (congenital) or renal conditions, diabetes mellitus, or receiving immunosuppressive or cytotoxic drugs. The study protocol was approved by the Ethics Committee of the Venizelion General Hospital, the regional Medical Association and the Ministry of Education of Greece. The study was in accordance with the standards of the 1964 Declaration of Helsinki. Two hundred and eighty-seven children were finally entered in the study (74.5 % of the initial number of students). The reason for noninclusion in the majority of excluded subjects was unwillingness to participate.

Anthropometric data

Measurements were performed in May 2011. The children were lightly dressed with no shoes at the time of examination. Weight was measured with a digital scale (±100 g). Height was recorded with a portable commercial stadiometer with the back in a straight position and with the shoulders loose with accuracy of ±0.5 cm. Waist circumference (WC) measurements was performed with a plastic metric band at the half distance between the iliac crest and the lower ribs level. The measurements from a previous study (2007) in 5,321 children in Crete were used as reference values for WC percentiles [17]. BMI was calculated by weight (in kilograms) divided by the height squared (in square meters). Children were classified as “of normal weight,” “overweight,” and “obese” according to the standard definition for child overweight and obesity—IOTF curves for BMI regarding age and gender [7]. The cutoff values for “overweight” and “obese” children were taken from curves which correspond to adult (at the age of 18 years) BMI values 25 and 30 kg/m2 [7].

Blood pressure and AS measurements

BP was measured according to the guidelines of the European Society of Hypertension for hypertension management in children and adolescents [20]. Assessment of ΑS was made according to the recommendations of the American Heart Association for subclinical atherosclerosis in children and adolescents [33]. Hypertension classification was done according to the National Heart, Lung, and Blood Institute percentiles regarding age, gender, and height [23]. Subjects were having a 10-min rest at least before measurements. Measurements were performed on the left arm, in a seated position, without talking, in the morning hours (1000–1300). The cuffs used were appropriate for each individual’s arm circumference.

The R6.5 Cardiovascular Monitor (PulseCor®, Auckland, New Zealand) was used for BP and AS assessment. This device measures the BP using the oscillometric method from an upper arm cuff and incorporates a POEM2 module (Welch Allyn, Skaneateles Falls, NY, USA) which complies with the Association for the Advancement of Medical Instrumentation (AAMI SP10) requirements and receives A/A in the British Hypertension Society evaluation protocol [19] for measurement accuracy. Two measurements of BP (with 5-min interval) were taken and the mean was calculated, and one set of suprasystolic recordings for 10 s following each BP measurement was performed. From the measurement of the brachial BP and the analysis of the brachial pulse wave, it was possible to determine the augmentation index (AI), the peripheral pulse pressure/central pulse pressure (PPP/CPP), and the reflected wave transit time corrected for height (RWTT/Height) as described below. These parameters were used as indices of AS.

The theory of wave reflections implies that the total pressure at any location in the arterial tree is made up of forward- and backward-going pressure waves. The R6.5 measures the total pressure waveform using the suprasystolic measurement technique. In this method, the signals are recorded with the cuff pressure approximately 30 mmHg above the systolic pressure. Pressure fluctuations within the artery are transferred by the arm to cause corresponding pressure fluctuations in the cuff. The measured pressure fluctuations can then be analyzed to identify points characteristic of the forward- and backward-going pressure waves. The R6.5 records approximately 10 s of suprasystolic signals, corresponding to usually at least one respiratory cycle. These signals are then processed to give information about the average suprasystolic pulse over the 10 s and also the variability of the pulses over the 10 s. Six feature points with coordinates pressure (p) and time (t) as shown in Fig. 1 are automatically determined. AI at the level of the brachial artery is calculated from the formula: \( {\hbox{AI}} = \left( {{p_3} - {p_0}} \right)/\left( {{p_1} - {p_0}} \right) \), where p0, p1, and p3 denote pressure values at time points t0, t1, and t3 (Fig. 1). This index describes the relative height of the reflected pressure wave when compared to the incident waveform. The AI in the brachial level is an index of the local wave reflections and it is related to the AS of the large arteries (mainly the aorta) [6].
https://static-content.springer.com/image/art%3A10.1007%2Fs00431-012-1735-3/MediaObjects/431_2012_1735_Fig1_HTML.gif
Fig. 1

Feature points on a typical suprasystolic pulse on the R6.5 Cardiovascular Monitor screen. t0, p0 start of the pulse, t1, p1 peak of the incident wave, t2, p2 trough between incident and reflected wave, t3, p3 peak of the reflected wave

The time–domain relationship between the oscillatory pressure in the aorta (pt0) and oscillatory pressure under the essentially occluding cuff (pt3) can be calculated by modeling the time for the forward and backward waves to propagate from the cuff to the aorta, resulting in the formula:
$$ {p_{{t0}}}(t) = \frac{b}{{b + 1}}{p_{{t3}}}\left( {t - {\hbox{d}}t} \right) + \frac{b}{{b + 1}}{p_{{t3}}}\left( {t + {\hbox{d}}t} \right) $$
where t is the time variable, b is the proportion of a pressure wave travelling towards the cuff that is reflected back from the cuff, and dt is the time taken for a pressure wave to travel from the subclavian root to the cuff occlusion [19]. Clinical evaluation of the estimation method was performed in 16 adult patients undergoing elective diagnostic cardiac catheterization. In these subjects, central pressures derived by this model were compared to simultaneous invasive catheter measurements [19]. There was good agreement regarding systolic BP (difference [mean ± standard deviation], 1 ± 7 mmHg) and diastolic BP (difference, 4 ± 4 mmHg). The estimates of central pressures by the method presented exceed the requirements of AAMI SP10 (requiring agreement better than 5 ± 16 mmHg). In another validation study, 30 adult individuals underwent consecutive radial (by use of SphygmoCor) and brachial (by use of R6.5 Monitor [PulseCor]) waveform measurements [26]. The mean difference was within the AAMI standards (<5 ± 8 mmHg). Bland–Altman analysis showed no systematic bias between devices across the range of BPs measured.

Validation studies in children for either SphygmoCor or R6.5 Monitor (Pulsecor) have not been performed so far. The SphygmoCor pulse analysis has been used as a tool for the assessment of PWV in a study of 573 children of 10 years of age [27]. The R6.5 Monitor [PulseCor] is used for AS assessment in children for the first time in the present study.

The ratio PPP/CPP was also calculated by the formula: \( {\hbox{PPP}}/{\hbox{CPP}} = \left[ {{\hbox{peripheral systolic pressure}} - {\hbox{peripheral diastolic pressure}}} \right]/\left[ {{\hbox{central systolic pressure}} - {\hbox{central diastolic pressure}}} \right] \). Physiologically, there is augmentation of the pulse pressure in the arterial tree from central to peripheral sites [35]. This ratio depends on cardiac systolic ejection properties and on the augmentation of central BP due to reflected wave (thus depending on AS) [35]. For the same level of peripheral systolic BP, a greater reduction in the PPP/CPP is an indication for greater afterload during systole. This was shown in a study in which, during static exercise (hand grip), the workload posed on the left ventricle (expressed as the change in CPP) was relatively higher than that posed during dynamic exercise (given the same pulse pressure change in the periphery) [21].

The RWTT/Height was also used as an index of AS. The RWTT is the time period Τ2 − Τ0 (Fig. 1) which is the time of the return of the reflection wave. The traditional theory assumes that the main reflection site is at the bifurcation of the aorta. In contrast to this concept, a recent study with devices using “suprasystolic measurements” (Pulsecor, Arteriograph) of computer simulation of the arterial pulse propagation showed that the major initial reflection site was at the level of the subclavian/brachial artery [31]. The RWTT/Height has an inverse relation with PWV and is used as an index of brachial AS, which has been shown to be related with the central (aortic) AS [6]. AI, PPP/CPP, and RWTT were used as parameters for the assessment of changes of central hemodynamic parameters during different types of exercise in adults in a recent study where the SphygmoCor was used [21].

Signal quality was assessed by the signal to noise (S/N) ratio (in decibels). Signal recordings with S/N values >3 were considered to be acceptable.

Assessment of the Mediterranean diet patterns

The degree of adherence to the Mediterranean diet was assessed by the KIDMED index (Mediterranean Diet Quality Index in children and adolescents) which was first used to assess dietary habits in a cohort of 3,850 children and young adults (2–24 years) in Spain [29]. The index includes 16 questions and it is based on principles sustaining Mediterranean dietary patterns, as well as those that undermine it. Questions denoting a negative connotation with respect to the Mediterranean diet were assigned a value of −1 and those with positive aspect +1. A total KIDMED score of 0–3 reflects a poor diet, whereas a score of 4–7 and 8–12 correspond to average and good adherence to the Mediterranean diet principles, respectively. The KIDMED index was validated in a recent study from Cyprus and it was found to correlate independently with diet quality in children (frequent consumption of seafood, fish, legumes, nuts, olive oil, leafy vegetables, low glycemic index foods, and unrefined foods) [16].

Statistical analysis

Data were tested for normality of distribution (box plot tests, skewness and curtosis, and QQ plots). Parametrical tests were used for variables with normal distribution. The Mann–Whitney U test was applied as a nonparametrical test. Continuous variables are expressed as the mean ± SD and categorical variables are expressed as percentages. Differences between two or more groups were assessed by the independent samples t test or one-way analysis of variance accordingly. Multivariable correlations were performed by using multiple linear regression models. SPSS Version 17 (SPSS Inc., Chicago, IL, USA) was used for the analysis. Statistical significance was inferred at a two-sided probability value <0.05.

Results

The measurements from 10 subjects were excluded from the analysis (bad signal quality/outliers). The data of 277 subjects (132 boys and 145 girls; mean age, 12 ± 8 years) were left for analysis. There was no difference in BMI between genders, although boys were taller and had higher WC values than girls (Table 1). In Table 2, it is shown that boys had higher peripheral systolic BP (108 ± 10.2 vs. 104.4 ± 9.5 mmHg, p = 0.003), central systolic BP (103.9 ± 11.8 vs. 98.9 ± 10.5 mmHg, p < 0.001), central mean BP (81.1 ± 6.8 vs. 79.3 ± 6.4 mmHg, p = 0.020), PPP (49.0 ± 8.3 vs. 44.3 ± 6.6 mmHg, p < 0.001), and CPP (39.8 ± 10.5 vs. 35.3 ± 8.8 mmHg, p < 0.001). There was no gender difference in the PPP/CPP (Table 3). Girls had higher values of AI than boys (27.8 ± 11.0 vs. 25.1 ± 11.9 %), with a difference that just failed to be statistically significance (p = 0.052). The RWTT/Height was higher in girls than boys (1.122 ± 0.071 vs. 1.098 ± 0.076 s/cm, p < 0.001).
Table 1

Anthropometric values among genders

 

Total (n = 277)

Boys (n = 132)

Girls (n = 145)

Mann–Whitney U test

Median

Range (min–max)

Median

Range (min–max)

Median

Range (min–max)

p value

Weight (kg)

55.4

31.8–101.9

56.4

36.6–101.9

54.7

31.8–84.6

0.457

Height (cm)

161.6

141.4–181.3

162.1

143.8–181.3

160.5

141.4–172.6

0.006

WC (cm)

75.3

54.5–106.3

77.0

54.6–106.3

73.4

55.2–100.1

0.000

BMI (kg/m2)

21.37

14.1–32.6

21.0

15.4–32.4

21.6

14.1–32.6

0.495

Table 2

BP parameters among genders

 

Total (n = 277), mean ± SD

Boys (n = 132), mean± SD

Girls (n = 145), mean ± SD

t test, p value

Peripheral systolic pressure (mmHg)

106.1 ± 10.0

108 ± 10.2

104.4 ± 9.5

0.003

Peripheral diastolic pressure (mmHg)

59.5 ± 7.7

58.9 ± 7.9

60.1 ± 7.4

0.215

Peripheral mean pressure (mmHg)

75.1 ± 7.4

75.2 ± 7.5

74.9 ± 7.4

0.740

Peripheral pulse pressure (mmHg)

46.6 ± 7.8

49.0 ± 8.3

44.3 ± 6.6

0.000

Heart rate (bpm)

85.0 ± 12.9

83.3 ± 12.5

86.6 ± 13.2

0.033

Central systolic pressure (mmHg)

101.3 ± 11.4

103.9 ± 11.8

98.9 ± 10.5

0.000

Central diastolic pressure (mmHg)

63.8 ± 5

64.0 ± 5.1

63.6 ± 4.8

0.495

Central mean pressure (mmHg)

80.1 ± 6.6

81.1 ± 6.8

79.3 ± 6.4

0.020

Central pulse pressure (mmHg)

37.5 ± 9.9

39.8 ± 10.5

35.3 ± 8.8

0.000

Table 3

AS parameters and KIDMED scores among genders

 

Total (n = 277)

Boys (n = 132)

Girls (n = 145)

t testa, p value

Mean ± SD

Median

Range

Mean ± SD

Median

Range

Mean ± SD

Median

Range

KIDMED index

6.6 ± 2.2

7

0–12

6.48 ± 2.22

7

0–12

6.82 ± 2.17

7

1–12

0.197

ΑΙ (%)

26.5 ± 11.5

25.4

−0.9 to 60.4

25.1 ± 11.9

24.5

−0.09 to 60.4

27.8 ± 11.0

26.2

1.5–58.5

0.052

PPP/CPP

1.28 ± 0.18

1.26

0.94–2.11

1.26 ± 0.19

1.26

0.96–2.11

1.29 ± 0.17

1.27

0.94–1.82

0.268

RWTT/Height (s/cm)

1.11 ± 0.07

1.10

0.91–1.29

1.09 ± 0.08

1.08

0.92–1.28

1.12 ± 0.07

1.12

0.91–1.29

0.007

AI augmentation index, PPP/CPP peripheral pulse pressure/central pulse pressure, RWTT/Height reflected wave transit time to height

aDifference of means between genders

The values of BMI, WC, and BP in both genders are shown in Table 4. The total sum of percentages of overweight and obese children was 43.3 %. The percentages of WC and BP in the <90th percentile, 90–95th percentile, and >95th percentile ranges were close to the expected values for age and gender.
Table 4

Values of BMI, WC, and BP among genders

 

ΒΜΙ (IOTF cutoffs [7])

Normal (cutoff for adult <25 m2/kg)

Overweight (cutoff for adult ≥25– ≤ 30 m2/kg)

Obese (cutoff for adult >30 m2/kg)

Total

Boys

76 (57.6 %)

34 (25.7 %)

22 (16.6 %)

132

Girls

81 (55.9 %)

50 (34.5 %)

14 (9.7 %)

145

Total

157 (56.7 %)

84 (30.3 %)

36 (13.0 %)

277

 

WC (waist percentiles [17])

<90th percentile

90–95th percentile

>95th percentile

Total

Boys

100 (75.8 %)

9 (6.8 %)

23 (17.4 %)

132

Girls

119 (82.1 %)

16 (11.0 %)

10 (6.9 %)

145

Total

219 (79.1 %)

25 (9.0 %)

33 (11.9 %)

277

 

Blood pressure (according to NHBLI percentiles [22])

<90th percentile

90–95th percentile

>95th percentile

Total

Boys

121 (91.7 %)

8 (6.6 %)

3 (2.3 %)

132

Girls

138 (95.2 %)

2 (1.4 %)

5 (3.4 %)

145

Total

259 (93.5 %)

10 (3.6 %)

8 (2.9 %)

277

Ιn univariate analysis, a significant negative correlation between PPP/CPP and mean arterial pressure (r = −0.414, p < 0.001) and between PPP/CPP and BMI (r = −0.233, p < 0.001) was found. Table 5 shows the comparison between three groups of BMI (normal, overweight, and obese according to IOTF cutoffs) and WC (<90th percentile, ≥90th percentile and ≤95th percentile, and >95th percentile). Groups with higher values of BMI and WC had significantly higher values of peripheral systolic, mean and central systolic, diastolic, and mean BPs, lower PPP/CPP values, and lower KIDMED index compared with groups of normal BMI and WC values. The RWTT/Height was also higher in the group of the <90th percentile WC values compared to the group with the >95th percentile WC values.
Table 5

Comparison between three groups of BMI (normal, overweight, and obese according to IOTF cutoffs) and WC (<90th percentile, ≥90th percentile and ≤95th percentile, and >95th percentile)

 

Normal (cutoff for adult <25 m2/kg)

Overweight (cutoff for adult ≥25– ≤ 30 m2/kg)

Obese (cutoff for adult >30 m2/kg)

ANOVA

n = 157

n = 84

n = 36

F

p value

Peripheral systolic (mmHg)

103.4 ± 9.8

108.2 ± 8.8

113.36 ± 7.9

20.305

0.000

Peripheral diastolic (mmHg)

58.3 ± 7.57

60.9 ± 7.14

61.9 ± 8.314

5.093

0.007

Peripheral mean (mmHg)

73.4 ± 7.50

76.58 ± 6.56

79.1 ± 6.72

11.495

0.000

Central systolic (mmHg)

98.3 ± 10.97

103.4 ± 10.28

110.0 ± 10.02

19.605

0.000

Central diastolic (mmHg)

62.8 ± 4.90

64.8 ± 4.69

66.0 ± 4.83

8.850

0.000

Central mean (mmHg)

78.4 ± 6.55

81.6 ± 5.93

84.8 ± 5.21

18.672

0.000

AI (%)

29.2 ± 0.11

25.7 ± 0.12

24.4 ± 0.10

1.156

0.316

PPP/CPP

1.31 ± 0.20

1.26 ± 0.15

1.20 ± 0.11

6.378

0.002

RWTT/Height (s/cm)

1.11 ± 0.08

1.10 ± 0.07

1.10 ± 0.06

0.588

0.556

KIDMED index

6.67 ± 2.12

6.97 ± 2.14

5.8 ± 2.43

3.161

0.044

 

Waist

<90th percentile

≥90th percentile and ≤95th percentile

>95th percentile

ANOVA

n = 219

n = 25

n = 33

F

p value

Peripheral systolic (mmHg)

104.4 ± 9.8

109.5 ± 7.9

114.9 ± 7.4

20.09

0.000

Peripheral diastolic (mmHg)

58.5 ± 7.4

62.4 ± 5.3

63.8 ± 8.8

9.37

0.000

Peripheral mean (mmHg)

73.88 ± 7.2

78.3 ± 5.1

80.6 ± 7.3

16.04

0.000

Central systolic (mmHg)

99.5 ± 11.3

104.4 ± 9.0

110.5 ± 9.1

15.6

0.000

Central diastolic (mmHg)

63.1 ± 4.8

65.6 ± 4.2

67.4 ± 5.0

13.7

0.000

Central mean (mmHg)

78.9 ± 6.5

82.5 ± 5.3

86.0 ± 5.21

20.38

0.000

AI (%)

27.3 ± 11.7

25.9 ± 10.0

22.5 ± 10.7

2.53

0.081

PPP/CPP

1.30 ± 0.2

1.23 ± 0.12

1.21 ± 0.14

3.73

0.025

RWTT/Height (s/cm)

1.12 ± 0.08

1.09 ± 0.06

1.08 ± 0.05

3.95

0.020

KIDMED index

6.82 ± 2.23

6.16 ± 2.06

5.87 ± 2.35

3.285

0.039

AI augmentation index, PPP/CPP peripheral pulse pressure/central pulse pressure, RWTT/Height reflected wave transit time to height ratio

Multiple linear regression was performed in order to search for multivariate correlations in several models (Table 6). Independent variables were AI, PPP/CPP, and RWTT/Height and dependent variables were gender (coded: male = 1, female = 2), heart rate, peripheral mean pressure, BMI, WC, and KIDMED index. The KIDMED index was found to have a statistically independent negative correlation with AI. The major determinants of AI were found to be heart rate and height (negative correlations). The variables that contributed most to the variability of PPP/CPP and RWTT/Height were heart rate and peripheral mean pressure. Female gender had a positive correlation with AI. BMI correlated negatively only with PPP/CPP.
Table 6

Multiple linear regression in several models

Dependent variables

Adjusted R2

Gender

Heart rate

Peripheral mean pressure

Height

BMI

WC

KIDMED index

Beta

p value

Beta

p value

Beta

p value

Beta

p value

Beta

p value

Beta

p value

Beta

p value

AI (%)

0.330

0.136

0.015

−0.508

0.000

0.110

0.063

−0.370

0.000

−0.045

0.615

0.114

0.909

−0.114

0.026

PPP/CPP

0.359

0.056

0.306

0.427

0.000

−0.574

0.000

0.210

0.000

−0.182

0.039

0.105

0.274

0.040

0.420

RWTT/Height

0.184

0.064

0.297

−0.126

0.032

−0.303

0.000

  

0.112

0.078

−0.334

0.001

0.078

0.166

Dependent variables: AI augmentation index, PPP/CPP peripheral pulse pressure/central pulse pressure, RWTT/Height reflected wave transit time to height ratio. Gender codes: male=1, female=2

Discussion

In this study in a healthy pediatric population, the association of indices of AS (AI, PPP/CPP, and RWTT/Height) with anthropometric measures (BMI and WC), BP values, and patterns of dietary habits was explored. The major findings were that children with higher BMI and WC had higher peripheral and central BPs accompanied with lower PPP/CPP values and RWTT/Height (between WC groups) (Table 5), and also in multiple regression analysis, BMI was found to correlate negatively with PPP/CPP (Table 6). Adherence to the Mediterranean diet was also found to have an independent correlation with AI in this cohort (Table 6). Heart rate, height, and BP were found to be more important factors relating to indices of AS. Girls tended to have higher AI and RWTT/Height than boys (Table 3).

The association of childhood obesity with AS has been investigated in some studies by using different methodologies and research populations. In a study of 1,306 subjects from 10 up to 86 years old, a consistent correlation of indices of AS (assessed by ultrasound) with BMI across the whole age range was found [37]. There was no mention, though, of the number of participants of younger age in this cohort. Also, two studies in adolescents from Korea (262 adolescents aged 12–18 years old) [11] and Japan (754 adolescents 15–17 years old) [22] showed an association between PWV and BMI. In contrast, in two other studies from Japan (970 children/adolescents 9–17 years old) [24] and Philadelphia, USA (205 adolescents/young adults 12–21 years old) [8], no correlation was found. All the above studies included healthy subjects from the general population. In two studies comparing obese with normal weight prepubertal children by using the ultrasound method, it was found that the obese group had lower compliance and higher incremental elastic modulus in the common artery [2, 30]. In a population study in 573 children of 10 years of age, it was found that PWV (assessed by SphygmoCor pulse analysis) was correlated in multivariate analysis with BMI, WC, and body fat percentage [27]. Adiposity and physical activity accounted for a small proportion in the variability of PWV in this study and the final hypothesis was that, in at such young age, the predominant factors for AS are the genetic ones [27].

The abovementioned studies show diversity in the results on the association of childhood obesity and AS. This may be attributed to differences in population samples (community studies vs. studies comparing “healthy” to “nonhealthy” subjects) and also differences in age and methodology among studies. For example, studies in communities may not reveal existing correlations because they may include a small number of subjects with the risk factor studied [11]. In our cohort, BMI was found to be associated only with PPP/CPP in multivariate model with a low coefficient beta value (meaning that BMI was significant but not a strong predictor of PPP/CPP). The other indices of AS studied were not found to be associated with BMI. In addition, the results shown in Table 5 indicate that children within the upper range of BMI and WC have a more adverse risk profile compared to those with normal BMI and WC. Overall, our findings add to the existing evidence that obesity in childhood exerts a small but significant detrimental effect on arterial function even from the age of 12 years.

In the present study, it was shown that PPP/CPP had a negative correlation with peripheral mean arterial pressure. This shows that an increase in peripheral BP is associated with a decrease in the ratio of PPP to CPP and in greater workload posed on the left ventricle than evident from the change in peripheral BP alone. This phenomenon was more pronounced in the obese vs. the lean children since PPP/CPP had also a negative correlation with BMI. Heart rate and height were also found to have negative correlations with AI in this study which is in accordance with results from other studies in adults [35] and in adolescents [22]. This can be explained by alterations in the pulse waveform (and consequently in the effect of the reflected wave) in the case of tachycardia and by the fact that the pulse travels along a greater distance in the case of a tall stature, respectively.

Female gender was found to correlate positively with AI in the multivariate analysis. Nevertheless, the results that PPP/CPP was not statistically different between genders and also that the RWTT/Height was higher in girls compared to boys do not support the hypothesis that AS is higher in girls. This finding corroborates results from a previous study in children of 8 years old, in which no difference in AS between genders was shown, although higher AI values were found in girls compared to boys [5].

The high prevalence of overweight (30.3 %) and obese (13.0 %) children was not a surprising finding in our study. Similar figures have been reported in a recent study from Ioannina (Western Greece) [4]. Through the last two decades, there has been an alarming trend for increasing obesity in this country. Previous studies have shown that Greece has a predominant place with regard to childhood obesity, since it has been reported to have one of the highest prevalences worldwide with significant rising trends [15]. This is also followed by a secular trend for increasing body height in schoolchildren in Greece [25] These trends explain the wide range of the anthropometric measures (weight, height, BMI, and WC) which were observed in our cohort.

Abandoning of the Mediterranean diet model and spending too much time in sedentary activities like TV or the Internet have been proposed as important factors for the spread of epidemic of obesity among children and young teenagers [4]. Data on the association of AS and dietary habits in childhood are very scarce in the literature. In a cohort study of 93 10-year-old children, it was shown that a positive relation existed between AS and dietary fat energy percentage and duration of breast feeding [28]. In a recent study of 1,622 subjects followed up for 27 years, it was shown that fruit and vegetable consumption in childhood was inversely associated with adulthood PWV [1]. The findings of our study show for the first time that there is an association of AI with the KIDMED index, which is a relatively new tool for the assessment of adherence to the Mediterranean diet, and this association was independent of obesity status.

Limitations of the study

The strength of the study is that our study was performed on a well-characterized sample of Greek children of the same age with good participation rate. A limitation of our study is that Tanner stage of puberty was not assessed in our subjects. Given that all measurements were performed at school, it was considered probable that additional tests would decrease substantially the participation rate. Data from literature show that the effect of puberty on AS may be different among genders: In girls, AS decreases after the onset of puberty, while in boys, the reverse has been observed [3]. Since the children in our study were within the transition age to puberty (12.8 years), it can be hypothesized that measurements in younger or older children could produce different results. Thus, the use of the term “children” may not actually describe precisely the developmental stage of all participants in our study, since a significant number of them (especially girls) may have already entered into adolescence, resulting in some degree of heterogeneity in our study cohort. Another limitation is that we used a new device (R6.5 Cardiovascular Monitor, PulseCor) that has been validated for BP measurement (AAMI (SP10) and protocol of the British Hypertension Society), but not for the assessment of AS in children, although there are validation studies in adults [19, 26]. The SphygmoCor pulse contour analysis (which has also been used in the research field of childhood AS [5, 27]) also lacks validation, since the transfer factor used applies only to adults. Other types of methodologies (like ultrasound measurements of artery geometry and function [2] or pulse and PWV assessment by an optical method via an infrared transducer [2]) are research tools necessitating sophisticated equipment and expertise in their use, and therefore, are rather inappropriate for every day clinical practice. On the other hand, the R6.5 offers an easy and quick assessment of BP and AS without posing technical difficulties which is an important factor in children.

Finally, we did not include information about the physical activity of the subjects in our study. The independent individual role of physical activity with regard to obesity and diet status on cardiovascular parameters and AS in children remains to be elucidated in future studies.

In conclusion, we performed assessment of obesity, BP, dietary patterns, and indirect indices of AS by peripheral pulse wave analysis in 12-year-old children. Obesity and diet habits were found to correlate independently with AS indices even from such young age. Obesity rates were alarmingly high in our cohort. Since obesity exerts its complications through tracking into adulthood, educational polices and healthy lifestyle strategies should be introduced more vigorously in children of preliminary schools nationwide.

Acknowledgments

The authors would like to express their thanks to Andrew Lowe from PulseCor®, Auckland, New Zealand for providing the necessary equipment (R6.5 Cardiovascular Monitor) to perform this study. This work was supported by a provision for equipment and administrative support from the Greek Hypertension Society.

Conflicts of interest

There was no conflict of interest for any of the authors in this study. The device used in this study (R6.5 Cardiovascular Monitor) was offered by PulseCor®, Auckland, New Zealand.

Copyright information

© Springer-Verlag 2012