There is a considerable debate about the potential influence of ‘fetal programming’ on cardiovascular diseases in adulthood. In the present prospective epidemiological cohort study, the relationship between birthweight and arterial elasticity in 472 children between 5 and 8 years of age was assessed. LAEI (large artery elasticity index), SAEI (small artery elasticity index) and BP (blood pressure) were assessed using the HDI/PulseWave™ CR-2000 CardioVascular Profiling System. Blood concentrations of glucose, total cholesterol and its fractions [LDL (low-density lipoprotein)-cholesterol and HDL (high-density lipoprotein)-cholesterol] and triacylglycerols (triglycerides) were determined by automated enzymatic methods. Insulin was assessed by a chemiluminescent method, insulin resistance by HOMA (homoeostasis model assessment) and CRP (C-reactive protein) by immunonephelometry. Two linear regression models were applied to investigate the relationship between the outcomes, LAEI and SAEI, and the following variables: birthweight, gestational age, glucose, LDL-cholesterol, HDL-cholesterol, triacylglycerols, insulin, CRP, HOMA, age, gender, waist circumference, per capita income, SBP (systolic BP) and DBP (diastolic BP). LAEI was positively associated with birthweight (P=0.036), waist circumference (P<0.001) and age (P<0.001), and negatively associated with CRP (P=0.024) and SBP (P<0.001). SAEI was positively associated with birthweight (P=0.04), waist circumference (P=0.001) and age (P<0.001), and negatively associated with DBP (P<0.001). Arterial elasticity was decreased in apparently healthy children who had lower birthweights, indicating an earlier atherogenetic susceptibility to cardiovascular diseases in adolescence and adult life. Possible explanations for the results include changes in angiogenesis during critical phases of intrauterine life caused by periods of fetal growth inhibition and local haemodynamic anomalies as a way of adaptation to abnormal pressure and flow.

INTRODUCTION

‘Fetal programming’ or the ‘hypothesis of fetal origin of adult diseases’ suggests that stimuli or injuries that occur during the critical periods of development may permanently modify the structure, physiology or metabolism of some organs, with smaller newborns consequently being more vulnerable to adverse environmental influences during adult life [1,2]. Prospective studies suggest that the effects of ‘programming’ are seen very early during the atherosclerotic process and are correlated with endothelial dysfunction, a process preceding atherosclerosis [35]. It has been demonstrated that low birthweight is able to predict the presence of endothelial dysfunction in children and adults, irrespective of other cardiovascular risk factors [4,6]. Intimal thickening of the endothelium [7] and compromised arterial distensibility [8] have been observed during infancy [3,8,9]. Although most clinical complications of atherosclerosis, such as myocardial infarction and cerebrovascular accidents, occur during a more advanced phase of life, autopsy of children and adolescents has confirmed the presence of atherosclerotic lesions in young individuals showing associations with vascular risk factors [10].

Over the past 20 years, most studies evaluating the relationship between nutrition and growth during early life and the occurrence of cardiovascular diseases during adult life have been conducted on young adults, probably due to the difficulty in estimating cardiovascular parameters in children.

Experimental and clinical evidence has confirmed the fundamental role of inflammation in the atherogenic process. Elevated levels of CRP (C-reactive protein) and other markers of inflammation are able to independently predict an increased risk of cardiovascular disease. According to Misra [11] and Yudkin [12], the relationship between inflammation, endothelial dysfunction and arterial disease emerges relatively early during childhood.

Non-invasive techniques for the evaluation of vascular dysfunction in childhood, which might be less time consuming and faster, are currently being developed. These techniques include tonometric evaluation of PWV (pulse wave velocity) and waveform and Doppler evaluation of skin microvascular response to endothelial agonists [13].

Arterial wave reflections are known to play an important role in cardiovascular health and disease [14,15]. The BP (blood pressure) contour or waveform is essentially a graphic representation of the elasticity of the body's arteries. Therefore the measurement of arterial elasticity has emerged as an important parameter for the evaluation of vascular diseases, which is mathematically defined as a change in blood vessel volume per heart beat [15].

Modern computerized techniques have recently been developed for the evaluation of elasticity based on the analysis of the arterial pulse waveform, which can be measured in the radial artery. Arterial elasticity and its respective two parameters [proximal elasticity of the aorta, C1 or LAEI (large artery elasticity index); and distal elasticity of small vessels and arterioles, C2 or SAEI (small artery elasticity index)] can be calculated using the Windkessel model of circulation [16], based on the analysis of arterial pressure waveforms during diastolic decline [17,18]

The present study has investigated the relationship between birthweight and elasticity of large and small arteries in a cohort of children born between 1998 and 2000 [19] in Jundiaí city, Brazil, comprising an age group that has not been investigated in epidemiological studies.

MATERIALS AND METHODS

An epidemiological prospective cohort study was conducted on 865 children from low-income families, who were followed-up from birth to 5–8 years of age. These children were born to women who participated in a cohort study between 1997 and 2000. Details of the study are given elsewhere [19].

Of the 865 mothers and children who comprised our cohort [19], 745 were located and invited to participate in the present study, resulting in a final sample of 649 children ranging from 5 to 8 years of age. The parents of the children signed an informed consent form and responded to a general questionnaire.

Data collection was performed between November 2004 and September 2006 and consisted of three phases. (i) On the basis of the information of the questionnaire of the first cohort study, the mothers who, at that time, lived in Jundiaí city and nearby towns were located. The mothers were invited to participate in the present study by telephone contact or home visit for those who did not have a telephone. A home interview was scheduled during which the study was explained and formal consent was obtained by signing the informed consent form. On the occasion of this home visit, a general questionnaire was applied to evaluate socio-economic and demographic characteristics and data regarding the morbidity of the children. (ii) The subjects were again contacted by telephone to schedule a day, time and place for the collection of anthropometric measurements (weight, height and waist circumference) and blood samples for the determination of total cholesterol, HDL (high-density lipoprotein)-cholesterol, LDL (low-density lipoprotein)-cholesterol, triacylglycerols (triglycerides), insulin, glucose and CRP. (iii) Arterial elasticity was evaluated with an HDI/Pulse Wave™ CR-2000 Research CardioVascular Profiling System (www.hdii.com/research/cvhealth.htm).

The body weight of the children was determined in the morning after a 10–12-h fast, and anthropometric measurements were obtained on the same day as the blood samples for the biochemical tests. The children were weighed on a portable Sohnle electronic scale, with a precision of 10 g. Weight gain was calculated by subtracting the birthweight of the child from his/her current weight. Height was measured with a SECA wall-mounted stadiometer, with a precision of 0.1 cm. Circumference measures were obtained with an unextendable glass fibre measuring tape. The anthropometric measurements were performed according to the recommendations of Cameron [20] and Jelliffe and Jelliffe [21].

Nutritional status was evaluated by calculation of the BMI {body mass index [weight (kg)/height2 (m2)]} of the children, according to the classification proposed by the National Center for Health Statistics at the CDC (Centers for Disease Control and Prevention; http://www.cdc.gov/GROWTHCHARTS/).

Immediately after collection of the blood samples for the determination of lipid profile, insulin, glucose and CRP, the material was separated by centrifugation at 87 g for 10 min (Tomy centrifuge model IC-15AN; Tominaga) and stored in a refrigerator at 4 °C for 24–48 h before the biochemical tests.

Total cholesterol and HDL-cholesterol were assayed by a colorimetric enzymatic method using the Bayer® Clinical Method for Advia 1200 Systems for cholesterol and HDL-cholesterol respectively. The serum LDL-cholesterol concentration was calculated using the formula of Friedwald [23]. Triacylglycerols were measured by an enzymatic reaction using the Bayer® Advia 1200 System for triacylglycerols. Total cholesterol, cholesterol fractions and triacglycerols were classified according to the recommendations of the First Brazilian Guidelines for the Prevention of Atherosclerosis during Childhood and Adolescence [24]. Glucose concentration was measured by an enzymatic method using the Bayer® Advia 1200 System for glucose hexokinase. Fasting glycaemia was classified based on the criteria currently adopted for the diagnosis of Type 2 diabetes, which are the same for adults and children [25]. Insulin was assayed by chemoluminescence in an Immulite® 2000 Analyser using the Immulite® 2000 Insulin kit (DPC). HOMA (homoeostasis model assessment) was used to predict insulin resistance [26]. For this, the HOMA index was calculated using the formula: HOMA=fasting glucose (mmol/l)×fasting insulin (μ-units/ml)/22.5. Insulin resistance was considered to be present when HOMA ≥2.5 [27]. Highly sensitive CRP was determined by particle-enhanced immunonephelometry using the CardioPhase hsCRP reagent (Dade-Behring).

Arterial elasticity (LAEI and SAEI) was measured with the HDI/Pulse Wave™ CR-2000 Research CardioVascular Profiling System, a non-invasive system for the evaluation of cardiovascular parameters. The examination was performed with the child lying on a gurney after a 10-min resting period and only if the child was not tense, agitated or crying. The HDI/Pulse Wave™ CR-2000 Research CardioVascular Profiling System measures and analyses the shape of the arterial pressure wave produced by the heart beat to evaluate artery elasticity. Pressure waveforms are collected from the radial artery by a BP module that uses an oscillometric method and by application of a special instrument that includes a sensor. The acquisition of calibrated data involves the coordinated use of a BP cuff on the left arm of the patient and direct contact with a piezoelectric acoustic sensor placed on the radial artery. The arterial sensor is placed on the pulse using a pulse stabilizer and adjusting the sensor to the largest percentage signal strength.

The calibration period, which ranged from 1 to 3 min depending on the time necessary to obtain stable waveforms, was followed by 30 s of analogue tracings of radial artery waveforms, which were digitized (about 200 samples/s) and then stored in a computer for the analysis of vascular elasticity. The mean graphical representation of all pulse waves obtained over the period of 30 s was derived as an algorithm as described by Cohn et al. [28]. The parameters of arterial elasticity were calculated from the decline in the DBP (diastolic BP) using a modification of the model of Windkessel [29]. Pulse contour analysis by the modified model of Windkessel separates the diastolic waveform into a declining sinusoidal oscillating wave and an exponential wave, and identifies the exponential wave as a function of the elasticity of large arteries and the sinusoidal oscillating wave as a measure of the elasticity of small arteries of the most peripheral vessels [30].

BP was classified according to the criteria proposed by the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents [31].

The following variables were analysed in the present study: birthweight, LAEI, SAEI, age, gender, gestational age, per capita income, BMI, waist circumference, weight gain, total cholesterol, HDL-cholesterol, LDL-cholesterol, triacylglycerols, glucose, insulin, HOMA index, SBP (systolic BP) and DBP.

Microsoft Office Excel 2003 and SPSS-15 for Windows (www.spss.com) were used for storage and statistical analysis of the data. For the analysis of categorical variables, the χ2 test with Yates correction was used, and Fisher's test was applied when the expected frequency was less than five. Student's t test or the Mann–Whitney test was used for the comparison of the mean of two independent variables. The relationship between birthweight, LAEI and SAEI and the variables of interest was determined using Pearson's or Spearman's correlation coefficient. A stepwise backward selection method was used for multiple linear regression analysis. The impact of birthweight and of the other independent variables (selected according to their biological importance, correlation coefficient and co-linearity) on LAEI and SAEI was determined by multivariate linear regression analysis. The explanatory variables LAEI and SAEI were analysed separately for each response variable because they were highly correlated.

The research has been carried out in accordance with the Declaration of Helsinki of the World Medical Association, and has been approved by the Ethics Committee of the School of Public Health, University of São Paulo.

RESULTS

Cardiovascular parameters were evaluated during the third phase of the study in a total of 472 children. Thus there was a 37% loss taking into account all mothers localized and interviewed since the first phase of the study, and a 47% loss when considering all mothers and children of the original cohort.

A significant difference (P=0.01) was observed between the mean age of the children included in the cohort (6.5±0.65 years) and that of children who did not conclude the study (5.8±1.4 years), but there was no difference in per capita income, birthweight or educational level of the mother.

With respect to gestational age, most children were born at term (≥37 and <42 weeks of gestation) and were female (Table 1). Approx. 6% of the children had a low birthweight (<2500 g). The percentage of children who were SGA (small-for-gestational age), according to the Williams curve [WHO (World Health Organization)-adopted growth curve] [32], was higher than that of children LGA (large-for-gestational age) (Table 1).

Table 1
Characteristics of the children in the present study

*Determined using a Williams curve [32]. SGA, ≤10th percentile; LGA, ≥90th percentile.

Variablen (%)Mean (S.D.)
Gestational age (weeks)  39.16 (1.32) 
 <37 12 (2.62)  
 37–42 442 (96.72)  
 >42 3 (0.66)  
  Total  457 (100.00)  
Gender   
 Male 218 (46.2)  
 Female 254 (53.8)  
  Total  472 (100.00)  
SGA*   
 Yes 62 (13.14)  
 No 410 (86.86)  
  Total  472 (100.00)  
LGA   
 Yes 22 (4.66)  
 No 450 (95.35)  
  Total  472 (100.00)  
Birthweight (g)  3208.16 (483.27) 
 1355–2499 27 (5.72)  
 2500–2999 124 (26.27)  
 3000–3499 195 (41.31)  
 3500–3999 103 (21.82)  
 4000–4710 23 (4.87)  
  Total  472 (100.00)  
Variablen (%)Mean (S.D.)
Gestational age (weeks)  39.16 (1.32) 
 <37 12 (2.62)  
 37–42 442 (96.72)  
 >42 3 (0.66)  
  Total  457 (100.00)  
Gender   
 Male 218 (46.2)  
 Female 254 (53.8)  
  Total  472 (100.00)  
SGA*   
 Yes 62 (13.14)  
 No 410 (86.86)  
  Total  472 (100.00)  
LGA   
 Yes 22 (4.66)  
 No 450 (95.35)  
  Total  472 (100.00)  
Birthweight (g)  3208.16 (483.27) 
 1355–2499 27 (5.72)  
 2500–2999 124 (26.27)  
 3000–3499 195 (41.31)  
 3500–3999 103 (21.82)  
 4000–4710 23 (4.87)  
  Total  472 (100.00)  

As shown in Table 2, most children were between 6 and 7 years of age. Approx. 67% of the participants had a per capita income of less than one MBW (minimum Brazilian wage; where 1 MBW=approx. US$77). According to the BMI categories by age (using the classification proposed by the National Center for Health Statistics at the CDC), 8.47% of the children were overweight, 14.19% had a risk of being overweight, 67.8% were normal and 9.54% were underweight; waist circumference ranged from 45 to 55 cm in 47.9% of the children.

Table 2
Socio-demographic, nutritional, biochemical and cardiovascular profiles of the children in the present study

*Percentiles that define low and high SBP and DBP in children and adolescents were determined according to the criteria proposed by the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents [31].

Variablen (%)Mean (S.D.)
Age (years)  6.57 (0.66) 
 5–6 98 (20.8)  
 6–7 248 (52.5)  
 7–8 126 (26.7)  
Per capita income (MBW)  0.95 (0.79) 
 <1.00 316 (66.9)  
 1.00–2.99 124 (26.3)  
 3.00–7.14 32 (6.8)  
BMI (kg/m2 24.04 (6.47) 
 Underweight 45 (9.5)  
 Normal weight 320 (67.8)  
 Risk of overweight 67 (14.2)  
 Overweight 40 (8.5)  
Waist circumference (cm)  56.57 (6.61) 
 45–55 226 (47.9)  
 55–65 198 (41.9)  
 65–90 48 (10.2)  
Glucose (mmol/l)  5.2 (0.4) 
 <3.9 2 (0.4)  
 3.9–5.6 398 (84.3)  
 5.6–6.9 72 (15.3)  
HOMA index  1.00 (0.80) 
 <2.5 454 (96.4)  
 ≥2.5 17 (3.6)  
Total cholesterol (mmol/l)  4.1 (0.8) 
 <3.9 191 (40.5)  
 3.9–4.4 126 (26.6)  
 ≥4.4 155 (32.9)  
HDL-cholesterol (mmol/l)  1.4 (0.3) 
 <1.2 71 (15.0)  
 ≥1.2 401 (85.0)  
LDL-cholesterol (mmol/l)  2.3 (0.7) 
 <2.6 333 (70.5)  
 2.6–3.4 119 (25.3)  
 ≥3.4 20 (4.2)  
Triacylglycerols (mmol/l)  0.8 (0.4) 
 <1.1 400 (84.8)  
 1.1–1.5 52 (11.0)  
 ≥1.5 20 (4.2)  
LAEI (ml/mmHg×10)  5.39 (2.42) 
 3.0–4.5 203 (43.1)  
 4.5–6.0 132 (28.0)  
 6.0–18 136 (28.9)  
SAEI (ml/mmHg×100)  3.70 (1.8) 
 0.5–2.5 123 (26.1)  
 2.5–4.5 223 (47.4)  
 4.5–15 126 (26.5)  
SBP (mmHg)  111.47 (12.82) 
 < 95th percentile* 306 (64.8)  
 ≥95th percentile* 166 (35.2)  
DBP (mmHg)  58.36 (9.32) 
 <95th percentile* 448 (94.9)  
 ≥95th percentile* 24 (5.1)  
Variablen (%)Mean (S.D.)
Age (years)  6.57 (0.66) 
 5–6 98 (20.8)  
 6–7 248 (52.5)  
 7–8 126 (26.7)  
Per capita income (MBW)  0.95 (0.79) 
 <1.00 316 (66.9)  
 1.00–2.99 124 (26.3)  
 3.00–7.14 32 (6.8)  
BMI (kg/m2 24.04 (6.47) 
 Underweight 45 (9.5)  
 Normal weight 320 (67.8)  
 Risk of overweight 67 (14.2)  
 Overweight 40 (8.5)  
Waist circumference (cm)  56.57 (6.61) 
 45–55 226 (47.9)  
 55–65 198 (41.9)  
 65–90 48 (10.2)  
Glucose (mmol/l)  5.2 (0.4) 
 <3.9 2 (0.4)  
 3.9–5.6 398 (84.3)  
 5.6–6.9 72 (15.3)  
HOMA index  1.00 (0.80) 
 <2.5 454 (96.4)  
 ≥2.5 17 (3.6)  
Total cholesterol (mmol/l)  4.1 (0.8) 
 <3.9 191 (40.5)  
 3.9–4.4 126 (26.6)  
 ≥4.4 155 (32.9)  
HDL-cholesterol (mmol/l)  1.4 (0.3) 
 <1.2 71 (15.0)  
 ≥1.2 401 (85.0)  
LDL-cholesterol (mmol/l)  2.3 (0.7) 
 <2.6 333 (70.5)  
 2.6–3.4 119 (25.3)  
 ≥3.4 20 (4.2)  
Triacylglycerols (mmol/l)  0.8 (0.4) 
 <1.1 400 (84.8)  
 1.1–1.5 52 (11.0)  
 ≥1.5 20 (4.2)  
LAEI (ml/mmHg×10)  5.39 (2.42) 
 3.0–4.5 203 (43.1)  
 4.5–6.0 132 (28.0)  
 6.0–18 136 (28.9)  
SAEI (ml/mmHg×100)  3.70 (1.8) 
 0.5–2.5 123 (26.1)  
 2.5–4.5 223 (47.4)  
 4.5–15 126 (26.5)  
SBP (mmHg)  111.47 (12.82) 
 < 95th percentile* 306 (64.8)  
 ≥95th percentile* 166 (35.2)  
DBP (mmHg)  58.36 (9.32) 
 <95th percentile* 448 (94.9)  
 ≥95th percentile* 24 (5.1)  

Impaired fasting glycaemia [5.6–6.9 mmol/l (70–100 mg/dl)] was observed in 72 (15.3%) children, and 17 (3.2%) had an altered HOMA index (≥2.5). Cholesterol was elevated [≥4.4 mmol/l (170 mg/dl)] [24] in 155 (32.8%) children. A total of 71 (15%) children had HDL-cholesterol below the recommended value [<1.2 mmol/l (<45 mg/dl)] and 20 (4.2%) were in the group of children with altered LDL-cholesterol [≥3.4 mmol/l (130 mg/dl)]. A total of 72 (15%) children had elevated triacylglycerols [≥1.5 mmol/l (130 mg/dl)].

No reference values for LAEI or SAEI exist in the international literature for children of the age group studied in the cohort of the present study. The prevalence of children with a SBP ≥95 percentile for gender, age and height [31] was very high (35.16%) (Table 2). SBP and DBP of children in this cohort are shown in Table 2.

Table 3 shows the correlations between birthweight, ponderal index at birth, gestational age, demographic and socio-economic factors, and current anthropometric, biochemical and cardiovascular parameters of the children (5–8 years of age). Birthweight was significantly correlated with LAEI, SAEI and all of the anthropometric measurements investigated. In addition to the correlation with the anthropometric measurements, LAEI and SAEI were also correlated with one another and with insulin, HOMA, SBP and DBP.

Table 3
Correlation coefficients between birthweight, ponderal index at birth, gestational age, demographic and socio-economic factors, and anthropometric, biochemical and cardiovascular parameters of the children

BW, birthweight; PIB, ponderal index at birth (weight/length3); GA, gestational age; WG, weight gain; Weight, actual weight (kg); Height, actual height (m); WC, waist circumference; TC, total cholesterol; TAG, triacylglycerols; Glucose, fasting glycaemia; Insulin, fasting insulin; Age, actual age (years); Income, per capita income. *P<0.05 and **P<0.001.

VariableBWPIBGAWGWeightHeightBMIWCTCHDLLDLTAGGlucoseInsulinHOMACRPLAEISAEISBPDBPAgeIncome
BW  1.000                      
PIB −0.008  1.000                     
GA  0.008  0.999**  1.000                    
WG  0.170** −0.036 −0.036  1.000                   
Weight  0.258** −0.036 −0.035  0.996** 1.000                  
Height  0.224** −0.068 −0.067  0.722** 0.728**  1.000                 
BMI  0.207** −0.005 −0.004  0.850** 0.853**  0.277** 1.000                
WC  0.198** −0.018 −0.017  0.903** 0.903**  0.524** 0.881**  1.000               
TC  0.035  0.032  0.028  0.027 0.030 −0.004 0.056  0.0454  1.000              
HDL  0.008 −0.007 −0.008  0.029 0.031  0.003 0.051 −0.062  0.380**  1.000             
LDL  0.028  0.001 −0.001  0.032 0.035  0.003 0.054  0.052  0.888**  0.073  1.000            
TAG −0.004 −0.030 −0.033  0.082 0.080  0.018 0.104*  0.115*  0.229** −0.302**  0.149*  1.000           
Glucose  0.044  0.029 −0.029  0.177** 0.178**  0.110* 0.171**  0.149* −0.009  0.031 −0.028 −0.010 1.000          
Insulin −0.054  0.000  0.002  0.406** 0.394**  0.177** 0.424**  0.451**  0.040 −0.091  0.023  0.191** 0.266**  1.000         
HOMA −0.079 −0.008 −0.007  0.315** 0.301**  0.121** 0.334**  0.346**  0.030 −0.048  0.019  0.090* 0.310**  0.872**  1.000        
CRP −0.005 −0.060 −0.059  0.209** 0.205**  0.000 0.289**  0.265** −0.003 −0.165**  0.050  0.085 0.007  0.056  0.067  1.000       
LAEI  0.161** −0.043 −0.038  0.329** 0.338**  0.351** 0.207**  0.261**  0.028 −0.038  0.055  0.017 0.062  0.134*  0.093* −0.082  1.000      
SAEI  0.134* −0.032 −0.029  0.224** 0.232**  0.236** 0.152**  0.171**  0.020 −0.050  0.041  0.032 0.030  0.062  0.042 −0.029  0.433**  1.000     
SBP −0.058  0.012  0.009  0.304** 0.293**  0.206** 0.274**  0.305**  0.506  0.001  0.048  0.036 0.116*  0.200**  0.147*  0.181** −0.172** −0.111* 1.000    
DBP −0.064  0.001  0.000  0.195** 0.185**  0.178** 0.139**  0.184**  0.034  0.020  0.026  0.002 0.074  0.142*  0.113*  0.118* −0.007 −0.163** 0.733** 1.000   
Age −0.011 −0.061 −0.063  0.015 0.348**  0.561** 0.067  0.179**  0.010 −0.060  0.038  0.007 0.096*  0.041  0.014 −0.094*  0.210**  0.173** 0.060 0.056  1.000  
Income  0.044  0.025  0.023 −0.043 0.047 −0.028 0.108*  0.058  0.082  0.116*  0.056 −0.006 0.017 −0.037 −0.027  0.109*  0.018 −0.031 0.036 0.068 −0.180** 1.000 
VariableBWPIBGAWGWeightHeightBMIWCTCHDLLDLTAGGlucoseInsulinHOMACRPLAEISAEISBPDBPAgeIncome
BW  1.000                      
PIB −0.008  1.000                     
GA  0.008  0.999**  1.000                    
WG  0.170** −0.036 −0.036  1.000                   
Weight  0.258** −0.036 −0.035  0.996** 1.000                  
Height  0.224** −0.068 −0.067  0.722** 0.728**  1.000                 
BMI  0.207** −0.005 −0.004  0.850** 0.853**  0.277** 1.000                
WC  0.198** −0.018 −0.017  0.903** 0.903**  0.524** 0.881**  1.000               
TC  0.035  0.032  0.028  0.027 0.030 −0.004 0.056  0.0454  1.000              
HDL  0.008 −0.007 −0.008  0.029 0.031  0.003 0.051 −0.062  0.380**  1.000             
LDL  0.028  0.001 −0.001  0.032 0.035  0.003 0.054  0.052  0.888**  0.073  1.000            
TAG −0.004 −0.030 −0.033  0.082 0.080  0.018 0.104*  0.115*  0.229** −0.302**  0.149*  1.000           
Glucose  0.044  0.029 −0.029  0.177** 0.178**  0.110* 0.171**  0.149* −0.009  0.031 −0.028 −0.010 1.000          
Insulin −0.054  0.000  0.002  0.406** 0.394**  0.177** 0.424**  0.451**  0.040 −0.091  0.023  0.191** 0.266**  1.000         
HOMA −0.079 −0.008 −0.007  0.315** 0.301**  0.121** 0.334**  0.346**  0.030 −0.048  0.019  0.090* 0.310**  0.872**  1.000        
CRP −0.005 −0.060 −0.059  0.209** 0.205**  0.000 0.289**  0.265** −0.003 −0.165**  0.050  0.085 0.007  0.056  0.067  1.000       
LAEI  0.161** −0.043 −0.038  0.329** 0.338**  0.351** 0.207**  0.261**  0.028 −0.038  0.055  0.017 0.062  0.134*  0.093* −0.082  1.000      
SAEI  0.134* −0.032 −0.029  0.224** 0.232**  0.236** 0.152**  0.171**  0.020 −0.050  0.041  0.032 0.030  0.062  0.042 −0.029  0.433**  1.000     
SBP −0.058  0.012  0.009  0.304** 0.293**  0.206** 0.274**  0.305**  0.506  0.001  0.048  0.036 0.116*  0.200**  0.147*  0.181** −0.172** −0.111* 1.000    
DBP −0.064  0.001  0.000  0.195** 0.185**  0.178** 0.139**  0.184**  0.034  0.020  0.026  0.002 0.074  0.142*  0.113*  0.118* −0.007 −0.163** 0.733** 1.000   
Age −0.011 −0.061 −0.063  0.015 0.348**  0.561** 0.067  0.179**  0.010 −0.060  0.038  0.007 0.096*  0.041  0.014 −0.094*  0.210**  0.173** 0.060 0.056  1.000  
Income  0.044  0.025  0.023 −0.043 0.047 −0.028 0.108*  0.058  0.082  0.116*  0.056 −0.006 0.017 −0.037 −0.027  0.109*  0.018 −0.031 0.036 0.068 −0.180** 1.000 

To prevent co-linearity, according to the correlations shown in Table 3, the following variables were not included in the same model: waist circumference, BMI, weight gain and current weight of the children, SBP and DBP. After adjustment, the different variables were used in various models and the model with the highest adjusted R2 was chosen.

Table 4 shows the multivariate linear regression models including LAEI and SAEI as the response or dependent variables, and all of the other variables studied as independent variables. Statistically significant associations were observed between LAEI and birthweight, waist circumference, CRP, SBP and age. An increase in birthweight of one unit (1 g) resulted in an increase of 0.4×10−3 ml/mmHg×10 in LAEI. Significant associations were also observed between SAEI and birthweight, waist circumference, SBP and age of the child. An increase in birthweight of one unit (1 g) resulted in an increase of 0.34×10−3 ml/mmHg×100 in SAEI.

Table 4
Linear regression models considering LAEI and SAEI as outcomes

For LAEI, adjusted R2=0.191 (standard error=1.936); for SAEI, adjusted R2=0.091 (standard error=1.718). CI, confidence interval.

(a) LAEI
ModelCoefficientStandard error95% CIP value
Birthweight 0.4×10−3 0.19×10−3 0.9×10−5–0.6×10−3 0.036 
Waist circumference 0.095 0.015 0.066–0.124 <0.001 
CRP −0.044 0.019 −0.081–0.006 0.024 
SBP −0.044 0.007 −0.058–0.029 <0.001 
Age 0.058 0.011 0.036–0.080 <0.001 
(b) SAEI
ModelCoefficientStandard Error95% CIP value
Birthweight 0.34×10−3 0.16×10−3 0.95×10−5–0.67×10−3 0.044 
Waist circumference 0.044 0.013 0.019–0.069 0.001 
DBP −0.038 0.009 −0.055–0.021 <0.001 
Age 0.036 0.01 0.016–0.055 <0.001 
(a) LAEI
ModelCoefficientStandard error95% CIP value
Birthweight 0.4×10−3 0.19×10−3 0.9×10−5–0.6×10−3 0.036 
Waist circumference 0.095 0.015 0.066–0.124 <0.001 
CRP −0.044 0.019 −0.081–0.006 0.024 
SBP −0.044 0.007 −0.058–0.029 <0.001 
Age 0.058 0.011 0.036–0.080 <0.001 
(b) SAEI
ModelCoefficientStandard Error95% CIP value
Birthweight 0.34×10−3 0.16×10−3 0.95×10−5–0.67×10−3 0.044 
Waist circumference 0.044 0.013 0.019–0.069 0.001 
DBP −0.038 0.009 −0.055–0.021 <0.001 
Age 0.036 0.01 0.016–0.055 <0.001 

DISCUSSION

As far as we know, this is the first epidemiological study that evaluates arterial elasticity in children less than 8 years of age. The results point to a positive association between birthweight and arterial elasticity in children 5–8 years of age. However, the strength of the association is not high, considering the value of the adjusted R2 for LAEI and SAEI and the impact of the increase of birthweight on these outcomes.

Possible explanations for the results include: (i) changes in angiogenesis during the critical phases of intrauterine life caused by periods of fetal growth inhibition, and (ii) local haemodynamic anomalies as a way of adaptation to abnormal pressure and flow, causing structural alterations that may persist during postnatal life. With regard to explanation (i), the elastic property of the arteries mainly depends on the presence of a slow turnover scleroprotein in the vessel wall called elastin [33]. The synthesis rates of elastin in blood vessels reaches a peak during the perinatal period [3436], declining rapidly thereafter. In rats, a brief period of growth inhibition on day 15 of fetal life, a period characterized by rapid cell growth during the development of the wall of the aorta, induces persistent changes in the chemical composition of this artery, including a reduction in total elastin content [37]. Thus there appears to be a critical period during the development of the aorta and of other large arteries when elastin is deposited in the vessels. Any damage to the synthesis of adequate quantities of elastin during this period can apparently not be reversed afterwards. With regard to explanation (ii), the structure of the fetal artery appears to adapt to abnormal pressure and flow, with these structural adaptations persisting during postnatal life. Doppler ultrasonography of blood flow velocity waveforms in growth-restricted fetuses shows changes in numerous vessels, including the descending aorta. These alterations correspond to a redistribution of cardiac output which permits the maintenance of the supply of oxygen and nutrients to the brain at the cost of other organs [38,39]. These haemodynamic changes that occur during a period of rapid vascular development probably influence elastin synthesis rates. Cheung et al. [8] reported that children studied at a mean of 8 years of age who were born preterm and SGA had a higher mean systemic arterial pressure and structural alterations in the radial artery than children whose birthweight was appropriate for gestational age. The relative deficiency of elastin in vessels receiving less blood may increase the amount of collagen in the vessel wall, a phenomenon that tends to potentiate the increase in BP, predisposing to cardiovascular disease [40].

Other factors that also had an association with arterial elasticity were CRP, BP, age and waist circumference. In the present study, CRP was significantly associated with elasticity of the large arteries, indicating that inflammation is probably the initial phenomenon that triggers endothelial dysfunction. Emphasizing the importance of inflammation on atherosclerosis, Jarvisalo et al. [41] showed that children with high concentrations of CRP had low flow-mediated vasodilation compared with children with CRP levels below the detection limit. In a meta-analysis, Danesh et al. [42] reported a relative risk of 2.13 for the prediction of future coronary events in adult subjects who had CRP levels in the top third compared with those whose CRP levels were in the bottom third. In adult Caucasian subjects, the cut-off values of CRP for predicting cardiovascular risk are known [43,44], but the values are practically unknown for childhood and other ethnic groups. Definitive conclusions about whether childhood CRP levels alone may influence future cardiovascular risk require further investigation.

Negative associations were observed between arterial elasticity and BP, i.e. between LAEI and SBP and between SAEI and DBP. The prevalence of children in the cohort with a SBP ≥95th percentile was high (35.16%) [31]; however, the measurement of BP is more variable in children than in adults because of the biological mechanisms that regulate it [45,46]. Thus extreme caution is necessary for the interpretation of occasional BP measurements in children. Cohort studies have shown that, irrespective of the age at which BP is measured in certain individuals, they tend to remain in the same population groups, a trend observed since 6 months of age [47]. According to Martyn and Greenwald [48], this characteristic suggests that mechanisms that affect both the distribution of BP levels within certain population groups and the individual risk of hypertension in adult life operate from the youngest age.

Arterial stiffness is observed in patients with hypertension, but it is unclear whether arterial disease precedes or is a consequence of elevated BP levels [49,50]. According to some authors, a BP increase during childhood and in younger individuals is generally related to an increase in the activity of the sympathetic nervous system and in peripheral vascular resistance resulting from arterial thickening [51,52].

In contrast with what was expected, arterial elasticity was positively associated with age. Previous studies have shown that both LAEI and SAEI decrease with age [28,53]. Baskett et al. [54] demonstrated a decrease in arterial compliance with age measured by ultrasound in 658 subjects ranging from 5 to 50 years of age. However, apparently, the number of children between 5 and 7.5 years of age included in that study was small. Unfortunately, most studies in the literature are conducted on adults and investigations on children and adolescents are scarce and, if done, involve a wide age range. In fact, the HDI/PulseWave™ CR-2000 CardioVascular Profiling System only includes a reference standard for subjects aged 15 years or older.

The reasoning to explain our results is the close age range of the children included in the study (approximately half of them ranged in age from 6 years and 3 months to 7 years and 1 month) and physical activity. It should be emphasized that most children included in the Jundiaí cohort live in shanty towns, some of them known to be dangerous because of the presence of drug trafficking. Thus we believe that, in the case of most of the smaller children, activity was restricted to the home, which was characterized by limited space, whereas school children probably had a higher chance of partaking in sports at school or of performing more physical activity, including walking to the school.

In adults, the IMT (intima-media thickness) of large arteries, such as the carotid artery, depends on age and gender [55]. However, Sass et al. [56] have shown that carotid IMT was not affected by age or gender up to 18 years of age in a large number of healthy children and young adults between 10 and 25 years of age.

Some studies have shown that physical activity, especially aerobic exercise, tends to improve arterial elasticity. Reed et al. [57] reported that healthy children between 9 and 11 years of age in the highest aerobic fitness quartile had a greater LAEI than children in the two lowest quartiles. In that study, using the HDI/PulseWave™ CR-2000 CardioVascular Profiling System, the mean LAEI in the highest quartile was greater than that of children included in the Jundiaí cohort (11.0 and 9.6 ml/mmHg×10 respectively), whereas the mean SAEI were similar (7.1 and 7.24 ml/mmHg×100 respectively). However, the mean LAEI (9.97±2.2 and 5.34±2.15 ml/mmHg×10) and SAEI (6.45±1.7 and 3.70±1.80 ml/mmHg×100) reported in the study by Reed et al. [57] were greater than those observed in the present cohort.

In contrast with most reports in the literature, we observed a positive association between arterial elasticity and waist circumference. One possible explanation for this finding is the significant correlations between birthweight and waist circumference (r=0.198; P<0.001), birthweight and current weight (r=0.258; P<0.001), birthweight and BMI (r=0.207; P<0.001), and birthweight and weight gain (r=0.170; P<0.001), suggesting that children who are born small probably gained less weight up to 5–8 years of age. The percentage of underweight (9.54%) and overweight (8.47%) children was similar in the present study. Therefore obesity was not characterized as an important problem in this paediatric population compared with other studies reported in the literature [58,59]. Our findings should be analysed with caution as both obesity and malnutrition cause endothelial alterations.

Several previous studies have demonstrated endothelial dysfunction in obese children [6063]; however, in most of these studies, obesity was classified based on different cut-off values and elevated BMIs were selected, very high values compared with the children from our present study.

Schutte et al. [64], evaluating the relationship between nutrition and cardiovascular parameters in 1244 African children from 10 to 15 years of age, observed lower vascular compliance in children with the lowest BMI. Van-Rooyen et al. [65] found a lower arterial compliance in 192 stunted children compared with 583 normal children. Kruger et al. [66] examined the body composition of a group of 478 stunted and non-stunted children between 10 and 15 years of age and observed increased subcutaneous fat and waist circumference in stunted children compared with non-stunted children. A common mechanism whereby obesity and malnutrition are associated with endothelial dysfunction might be oxidative reactions that are crucial during all events that lead to atherogenesis [67,68].

Interpreted as a long-term adverse effect of malnutrition during the fetal period, the ‘hypothesis of fetal origin of adult diseases’ should signal the need for health policies that promote adequate maternal–fetal nutrition and infant growth. Decreased arterial elasticity has been related to hypertension, Type 2 diabetes, atherosclerosis and coronary diseases in adult life. The results of the present study show vascular alterations in young children, indicating potential problems in an apparently healthy population.

Abbreviations

     
  • BMI

    body mass index

  •  
  • BP

    blood pressure

  •  
  • CDC

    Centers for Disease Control and Prevention

  •  
  • CRP

    C-reactive protein

  •  
  • DBP

    diastolic BP

  •  
  • HDL

    high-density lipoprotein

  •  
  • HOMA

    homoeostasis model assessment

  •  
  • IMT

    intima-media thickness

  •  
  • LAEI

    large artery elasticity index

  •  
  • LDL

    low-density lipoprotein

  •  
  • LGA

    large-for-gestational age

  •  
  • MBW

    minimum Brazilian wage

  •  
  • SAEI

    small artery elasticity index

  •  
  • SBP

    systolic BP

  •  
  • SGA

    small-for-gestational age

We are very grateful to FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) for sponsoring this project (grant no. 04/04109-8), and to Dr Atul Singhal who very kindly agreed to discuss some of the steps of the study with us. We acknowledge the Secretary of Health and Education from Jundiaí city.

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