George S. Reusz, Orsolya Cseprekal, Mohamed Temmar, Éva Kis, Abdelghani... Abddelhalim Thaleb, Andrea Fekete, Attila J. Szabó, Athanase Benetos and... Reference Values of Pulse Wave Velocity in Healthy Children and...

Transcription

Reference Values of Pulse Wave Velocity in Healthy Children and Teenagers
George S. Reusz, Orsolya Cseprekal, Mohamed Temmar, Éva Kis, Abdelghani Bachir Cherif,
Abddelhalim Thaleb, Andrea Fekete, Attila J. Szabó, Athanase Benetos and Paolo Salvi
Hypertension. 2010;56:217-224; originally published online June 21, 2010;
doi: 10.1161/HYPERTENSIONAHA.110.152686
Hypertension is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2010 American Heart Association, Inc. All rights reserved.
Print ISSN: 0194-911X. Online ISSN: 1524-4563
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Reference Values of Pulse Wave Velocity in Healthy
Children and Teenagers
George S. Reusz, Orsolya Cseprekal, Mohamed Temmar, Éva Kis, Abdelghani Bachir Cherif,
Abddelhalim Thaleb, Andrea Fekete, Attila J. Szabó, Athanase Benetos, Paolo Salvi
Abstract—Carotid-femoral pulse wave velocity is an established method for characterizing aortic stiffness, an individual
predictor of cardiovascular mortality in adults. Normal pulse wave velocity values for the pediatric population derived
from a large data collection have yet to be available. The aim of this study was to create a reference database and to
characterize the factors determining pulse wave velocity in children and teenagers. Carotid-femoral pulse wave velocity
was measured by applanation tonometry. Reference tables from pulse wave velocities obtained in 1008 healthy subjects
(aged between 6 and 20 years; 495 males) were generated using a maximum-likelihood curve-fitting technique for
calculating SD scores in accordance with the skewed distribution of the raw data. Effects of sex, age, height, weight,
blood pressure, and heart rate on pulse wave velocity were assessed. Sex-specific reference tables and curves for age
and height are presented. Pulse wave velocity correlated positively (P⬍0.001) with age, height, weight, and blood
pressure while correlating negatively with heart rate. After multiple regression analysis, age, height, and blood pressure
remained major predictors of pulse wave velocity. This study, involving ⬎1000 children, is the first to provide reference
values for pulse wave velocity in children and teenagers, thereby constituting a suitable tool for longitudinal clinical
studies assessing subgroups of children who are at long-term risk of cardiovascular disease. (Hypertension. 2010;56:
217-224.)
Key Words: adolescence 䡲 blood pressure 䡲 children 䡲 vessels 䡲 pulse wave velocity
C
ardiovascular disease is the leading cause of death in
Western societies.1 Although there is ample evidence
that arteriosclerosis begins in childhood,2– 4 hard end points,
such as stroke and ischemic heart disease, are rare or virtually
lacking in the pediatric population. There is, thus, an increasing need to establish validated noninvasive tools to forecast
early arterial disease and to be able to characterize elevated
cardiovascular risk in youngsters.5
It has been widely recognized that aortic pulse wave
velocity (PWV) is a sensitive marker of arterial stiffness and,
consequently, of cardiovascular outcome.6 –9 However, large
multicenter clinical studies aimed at generating normal PWV
values and assessing the influence of anthropometric factors
on PWV in healthy children and teenagers are still lacking.
In our previous investigations, we showed that, in specific
patient populations with growth retardation, the use of agematched controls failed to reflect the true impact of cardiovascular disease on PWV. Rather, controls matched for both
age and height or normalized PWV data such as PWV Z score
or PWV/height should be used in such instances.10 –12
The aim of the present study was to create a reference
database and to characterize the factors determining PWV in
children and teenagers. To achieve this, the distribution mode
of PWV was evaluated in a large group of healthy children
and adolescents using a novel statistical approach, the LMS
method (where L indicates skewness, M indicates median,
and S indicates coefficient of variation), to calculate SD score
(SDS) values in accordance with the skewed distribution of
the raw data. In addition, the relationship among anthropometric factors, blood pressure (BP), heart rate (HR), and ageand height-normalized PWV was also analyzed. Finally, an
analysis assessing the correlation between novel PWV Z
scores for age and height was also performed.
Subjects and Methods
Subjects
A total of 1008 healthy children and teenagers (mean age: 15.2 years
[range: 6.5 to 19.9 years]; 495 males) was assessed between 2006
and 2009. Only children with no history of diseases affecting BP and
without current antihypertensive medication or other BP-affecting
drugs were included in the study.
Received March 4, 2010; first decision April 3, 2010; revision accepted May 19, 2010.
From the First Department of Pediatrics (G.S.R., O.C., E.K., A.F., A.J.S.), Semmelweis University, Budapest, Hungary; Telomere Cardiology Centre
(M.T.), Ghardaia, Algeria; Department of Internal Medicine (A.B.C., A.T.), University of Blida, Blida, Algeria; Department of Internal Medicine and
Geriatrics (A.B., P.S.), Institut National de la Santé et de la Recherche Médicale U961, University of Nancy, Nancy, France; Department of Internal
Medicine (P.S.), University of Bologna, Bologna, Italy.
G.S.R. and O.C. contributed equally to this article.
Correspondence to George S. Reusz, First Department of Pediatrics, Semmelweis University, Budapest, Str Bókay János 53-54, H-1083 Budapest,
Hungary. E-mail [email protected]
© 2010 American Heart Association, Inc.
Hypertension is available at http://hyper.ahajournals.org
DOI: 10.1161/HYPERTENSIONAHA.110.152686
217
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218
Hypertension
August 2010
A total of 450 Hungarian (12.4 years [range: 6.5 to 19.9 years];
214 males), 455 Italian (17.3 years [15.8 to 19.9 years]; 178 males),
and 103 Algerian (17.8 years [16.4 to 19.8 years]; all males) healthy
school children and adolescents participated in the study. The study
was conducted at 3 primary and 2 high schools in Hungary and in 1
high school in both Italy and Algeria, respectively.
Methods
Anthropometric measurements were performed by trained staff from
the school. Weight and height were measured with precision electronic scales and fixed stadiometers.
BP was measured by oscillometric devices (Omron M742E and
Omron 705IT, Omron Co). The technical validity of these monitors
has been confirmed previously in adults, children, and adolescents.13–15 The midarm circumference was measured and the cuff
width adapted accordingly (pediatric, standard adult, or oversized
cuff for midarm circumference of 17 to 22, 22 to 32, or 32 to 42 cm,
respectively). Systolic BP (SBP), diastolic BP (DBP), mean arterial
pressure (MAP), and heart rate (HR) were calculated as the mean of
the 3 measurements. MAP was calculated as (SBP⫹2⫻DBP)/3. To
allow for comparison between different age groups, SDSs were
calculated.
PWV was measured by applanation tonometry using the PulsePen
device (DiaTecne s.r.l.)16 interfaced with a computer, as described
previously.12,16 Briefly, the probe was connected to a hand-held ECG
unit while pressure and electrocardiographic signals were transmitted
to a computer. Aortic PWV was measured by sequential recordings
of the arterial pressure wave at the carotid and femoral arteries and
defined as the distance of the sampling sites divided by the time
difference between the rise delay of the distal and proximal pulse
according to the R wave belonging to the ECG qRs complex and
calculated by the software. The pulse wave was calibrated by
measuring BP immediately after each recording. To assess pulse
wave travel distance, surface tape measurements were performed
between the carotid site and the jugular notch and between the
jugular notch and the femoral site. The difference between these 2
distances was considered as the pulse travel distance.17 The measurement of transit time was discarded and repeated if BP and HR
varied by ⬎10% in the carotid and femoral sites. Recordings were
also discarded when the variability between consecutive systolic or
diastolic waveforms was ⬎10% or when the amplitude of the pulse
wave signal was ⬍80 mV.
To avoid possible methodological bias (center effect), a single
senior investigator (P.S.) trained and supervised the staff performing
all of the PWV measurements. All of the measurements were
performed twice to confirm reproducibility, with the resulting PWV
value consisting of the mean of both measurements. The intraobserver and interobserver coefficients of variation of distance measurements were 2.3% and 4.0%, respectively. The intraobserver and
interobserver coefficients of variation of PWV measurements were
5.7% and 6.1%, respectively.
Evaluation of Sex Differences
To assess sex differences in PWV, pairs composed of males and
females matched for age and height were formed and compared. The
maximum interindividual difference allowed within 1 pair was ⬍5
cm for height and ⬍1 year for age.
Normalization of PWV Data: The LMS Method
Age- and height-specific reference values for PWV were generated
by the LMS method,18 which characterizes the distribution of a
variable by its median (M), the coefficient of variation (S, ie, the
ratio of the SD and mean), and skewness (L) required to transform
the data to normality. Evaluation for these parameters is obtained by
a maximum-likelihood curve-fitting algorithm to the original data
plotted over the independent variable. In this study, 2 sets of tables
were created, one using age and the other height as independent
variables.
L, M, and S can be used to create percentiles (C␣) according to the
following equation (1):
C␣(t)⫽M(t)⫻关1⫹L(t)⫻S(t)⫻z␣兴1/L(t)
(1)
where M(t), L(t), and S(t) or C␣(t) indicate the corresponding values
of each parameter at a given age or height (t). z␣ is the normal
equivalent deviate corresponding with the centile (eg, ␣⫽50, z␣⫽0;
␣⫽75, z␣⫽0.674; ␣⫽90, z␣⫽1.282; ␣⫽95, z␣⫽1.645; and ␣⫽97,
z␣⫽1.881).
Equation 1 can be rearranged to convert an individual child’s
PWV value to the following SDS:
Z ie, SDS⫽{关Y/M(t)兴L(t)⫺1}/关L(t)⫻S(t)兴
(2)
where Y is the child’s individual parameter (PWV), and L(t), M(t),
and S(t) are the specific values of L, M, and S interpolated for the
child’s age or height.
Data Analysis and Statistics
The data analysis was performed using the STATISTICA 8.0 (Stat
Soft Inc). The Shapiro-Wilks test and normal probability plot
analysis were used to characterize the normality of data distribution.
Data with nonnormal distribution are presented as median (range),
whereas normally distributed data are expressed as mean and 95%
CI. SDSs for height, weight, body mass index, SBP, and DBP were
calculated using standard reference charts.19
Univariate correlation and regression models were performed to
assess the relationship between anthropometric data and PWV.
Major determinants of PWV were established in accordance with the
backward multiple regression model.
Anthropometric and clinical data were compared by Student t test
or by ANOVA where appropriate. The Mann-Whitney U test was
used to compare data with nonnormal distribution. A P⬍0.05 was
considered as statistically significant.
Ethics
The study protocol was approved by the local ethics committees.
Participants provided informed consent, and for underage subjects, a
parental written informed consent was obtained.
Results
The mean values of anthropometric and BP data of subject
groups from the 3 geographic regions were within the normal
limits of the 25th to 75th percentile range for both age and
sex. Furthermore, there was no significant difference between
PWV data obtained from the different regions within the
respective age quartiles. Thus, the data were subsequently
pooled for further analysis and summarized in Table 1.
The PWV of boys and girls was similar in the first 2 age
quartiles. In contrast, in the third and fourth quartiles, boys
had significantly higher PWV, as well as height and BP data,
compared with the girls. Sixty-six pairs of boys and girls
matched appropriately for age and height were ultimately
formed to evaluate the effect of sex on PWV. Their comparative analysis revealed no differences in anthropometric, BP,
or PWV data. However, age- and sex-specific SDSs for
height and weight of girls were significantly higher compared
with their matched counterparts (height SDS: ⫺0.081 [range:
⫺0.249 to 0.086] versus 0.962 [range: 0.762 to 1.161],
P⬍0.05; weight SDS: 0.034 [⫺0.140 to 0.208] versus 0.353
[0.188 to 0.518] P⬍0.05 for males and females, respectively;
please see also Table S1 in the online Data Supplement at
http://hyper.ahajournal.org). Thus, because of sex differences
in growth and height, the standardized values of PWV are
presented separately for sexes.
Univariate regression analysis revealed a strong positive
correlation among age, height, weight, SBP, DBP, MAP, and
PWV (r⫽0.47, 0.44, 0.39, 0.43, 0.33, and 0.43; P⬍0.00001).
Reusz et al
Table 1.
Reference Values of Pulse Wave Velocity
219
Epidemiological Data of 1008 Healthy Children and Teenagers According to Age Quartiles
Variable
Quartile 1
Quartile 2
Quartile 3
Quartile 4
6.55 to 9.91
9.92 to 13.27
13.28 to 16.63
16.64 to 19.99
Male
56
81
127
231
Female
65
73
125
250
Age range, y
No.
Age, y
Male
8.4 (8.2 to 8.7)
11.5 (11.3 to 11.7)
15.7 (15.5 to 15.8)
17.8 (17.7 to 17.9)
Female
8.3 (8.1 to 8.6)
11.5 (11.3 to 11.8)
15.6 (15.4 to 15.8)
17.8 (17.7 to 17.9)
Male
30.8 (28.8 to 32.7)
42.1 (40.1 to 44.2)*
63.6 (61.5 to 65.7)*
65.3 (63.9 to 66.7)*
Female
29.6 (27.6 to 31.6)
47.3 (43.5 to 51)
56.8 (55.0 to 58.7)
56.6 (55.6 to 57.5)
Weight, kg
Weight SDS
Male
0.585 (0.299 to 0.872)
0.317 (0.138 to 0.497)
0.293 (0.132 to 0.455)
⫺0.113 (⫺0.22 to ⫺0.006)
Female
0.334 (0.106 to 0.561)
0.645 (0.35 to 0.941)
0.242 (0.102 to 0.382)
⫺0.036 (⫺0.128 to 0.056)
Male
132.9 (131.1 to 134.7)
151.3 (149.5 to 153.1)
174.5 (173 to 175.9)*
176.1 (175.1 to 177)*
Female
130.8 (128.6 to 133.0)
151.8 (149.5 to 154.1)
165.4 (164.3 to 166.4)
165.3 (164.6 to 166)
Height, cm
Height SDS
Male
0.428 (0.207 to 0.648)
0.557 (0.380 to 0.733)
0.207 (⫺0.216 to 0.630)
0.032 (⫺0.102 to 0.167)
Female
0.222 (⫺0.017 to 0.460)
0.514 (0.258 to 0.771)
0.513 (0.363 to 0.664)
0.042 (⫺0.321 to 0.405)
2
BMI, kg/m
Male
17.3 (16.5 to 18.1)
18.3 (17.6 to 19)*
20.8 (20.3 to 21.4)
21.0 (20.6 to 21.4)
Female
17.0 (16.3 to 17.7)
20.1 (19.0 to 21.3)
20.7 (20.1 to 21.4)
20.7 (20.4 to 21.0)
BMI SDS
Male
0.495 (0.207 to 0.783)
0.213 (0.027 to 0.399)*
0.091 (⫺0.067 to 0.248)
⫺0.206 (⫺0.311 to ⫺0.101)
Female
0.337 (0.119 to 0.556)
0.556 (0.294 to 0.819)
0.078 (⫺0.059 to 0.215)
⫺0.117 (⫺0.191 to ⫺0.043)
Male
102.0 (99.8 to 104.2)
105.0 (103.1 to 107.0)
122.5 (120.5 to 124.5)*
127.4 (126.0 to 128.7)*
Female
102.0 (100.1 to 103.9)
105.3 (103.6 to 107.1)
114.2 (112.6 to 115.8)
114.3 (113.2 to 115.4)
Male
0.300 (0.088 to 0.512)
0.096 (⫺0.131 to 0.324)
0.713 (0.548 to 0.877)*
0.890 (0.759 to 1.022)*
Female
0.451 (0.257 to 0.645)
0.092 (⫺0.091 to 0.276)
0.297 (0.146 to 0.448)
0.194 (0.084 to 0.304)
SBP, mm Hg
SBP SDS
DBP, mm Hg
Male
62.7 (61.3 to 64.1)*
61.4 (60.2 to 62.6)*
64.7 (63.4 to 65.9)*
67.9 (66.9 to 68.9)*
Female
65.3 (63.8 to 66.7)
63.8 (62.4 to 65.2)
67.4 (66.1 to 68.7)
69.3 (68.4 to 70.3)
DBP SDS
Male
0.286 (0.165 to 0.407)*
0.018 (⫺0.158 to 0.122)
0.034 (⫺0.140 to 0.072)*
0.089 (0.003 to 0.175)*
Female
0.646 (0.488 to 0.804)
0.168 (0.023 to 0.312)
0.163 (0.048 to 0.279)
0.258 (0.172 to 0.345)
Male
75.8 (74.4 to 77.3)
75.9 (74.6 to 77.2)
83.9 (82.7 to 85.2)
87.7 (86.8 to 88.7)*
Female
77.5 (76.1 to 79.0)
77.6 (76.2 to 79)
83.0 (81.7 to 84.3)
84.3 (83.4 to 85.2)
MAP, mm Hg
HR, bpm
Male
85 (82 to 87)
76 (74 to 78)*
75 (72 to 78)
68 (67 to 70)*
Female
85 (82 to 87)
79 (76 to 81)
73 (71 to 75)
71 (70 to 73)
PWV, m/s
Male
4.396 (3.106 to 5.902)
4.740 (3.275 to 6.391)
5.243 (3.640 to 8.021)
5.538 (3.725 to 7.999)*
Female
4.496 (2.809 to 5.801)
4.779 (3.552 to 6.826)
5.113 (3.955 to 6.983)
5.335 (3.181 to 7.634)
Data are shown as mean and 95% CI except for PWV, which is expressed as median (range). N indicates No. of subjects; BMI, body mass index; bpm, beat per
minute.
*P⬍0.05 males vs females.
220
Hypertension
Table 2.
Variable
August 2010
LMS Values and Specific Percentile Limits for PWV According to Age and Height for Males and Females
N
L
M
S
5th
10th
25th
50th
75th
90th
95th
7
12
⫺0.383
4.348
0.127
3.559
3.715
3.998
4.348
4.743
5.141
5.402
8
17
⫺0.346
4.384
0.128
3.579
3.739
4.027
4.384
4.784
5.188
5.451
9
17
⫺0.309
4.428
0.129
3.607
3.770
4.064
4.428
4.836
5.246
5.513
10
22
⫺0.272
4.505
0.130
3.659
3.827
4.131
4.505
4.923
5.343
5.615
11
31
⫺0.234
4.615
0.131
3.739
3.913
4.228
4.615
5.046
5.477
5.758
12
25
⫺0.197
4.742
0.132
3.835
4.016
4.342
4.742
5.188
5.632
5.919
13
15
⫺0.160
4.876
0.133
3.934
4.122
4.461
4.876
5.336
5.793
6.089
14
18
⫺0.123
5.014
0.134
4.033
4.229
4.582
5.014
5.491
5.965
6.271
15
24
⫺0.086
5.162
0.136
4.136
4.342
4.712
5.162
5.660
6.153
6.471
16
62
⫺0.049
5.312
0.138
4.238
4.454
4.841
5.312
5.832
6.345
6.675
17
123
⫺0.012
5.451
0.141
4.326
4.552
4.958
5.451
5.994
6.530
6.874
18
75
0.026
5.586
0.145
4.399
4.638
5.066
5.586
6.158
6.721
7.082
19
54
0.063
5.713
0.149
4.461
4.713
5.164
5.713
6.316
6.909
7.288
7
17
1.223
4.340
0.133
3.368
3.588
3.948
4.340
4.724
5.064
5.265
8
19
1.096
4.449
0.131
3.478
3.694
4.053
4.449
4.841
5.192
5.400
9
17
0.968
4.564
0.130
3.593
3.807
4.165
4.564
4.965
5.326
5.543
10
26
0.842
4.679
0.129
3.707
3.918
4.276
4.679
5.087
5.459
5.684
11
26
0.719
4.783
0.127
3.811
4.020
4.377
4.783
5.199
5.582
5.814
12
18
0.600
4.862
0.127
3.891
4.098
4.454
4.862
5.285
5.677
5.918
13
22
0.487
4.924
0.126
3.955
4.159
4.513
4.924
5.353
5.755
6.003
14
19
0.379
4.987
0.127
4.015
4.218
4.573
4.987
5.424
5.837
6.093
15
11
0.276
5.055
0.127
4.076
4.279
4.635
5.055
5.503
5.929
6.195
16
70
0.177
5.134
0.128
4.141
4.346
4.706
5.134
5.595
6.037
6.316
17
119
0.081
5.236
0.130
4.222
4.429
4.796
5.236
5.712
6.175
6.469
18
103
⫺0.012
5.363
0.131
4.325
4.535
4.910
5.363
5.859
6.345
6.654
19
46
⫺0.106
5.508
0.132
4.446
4.659
5.042
5.508
6.022
6.531
6.858
120
5
⫺0.065
4.269
0.123
3.493
3.651
3.931
4.269
4.639
5.001
5.231
125
14
⫺0.077
4.324
0.124
3.530
3.691
3.978
4.324
4.704
5.076
5.314
130
14
⫺0.089
4.378
0.126
3.565
3.730
4.023
4.378
4.768
5.152
5.397
135
19
⫺0.100
4.432
0.128
3.598
3.766
4.067
4.432
4.833
5.229
5.483
140
26
⫺0.111
4.500
0.130
3.640
3.813
4.123
4.500
4.916
5.327
5.592
145
13
⫺0.121
4.597
0.134
3.701
3.881
4.203
4.597
5.033
5.465
5.743
150
16
⫺0.129
4.726
0.137
3.786
3.974
4.312
4.726
5.186
5.644
5.939
155
22
⫺0.137
4.879
0.140
3.889
4.086
4.442
4.879
5.365
5.850
6.164
160
25
⫺0.143
5.033
0.143
3.996
4.202
4.574
5.033
5.545
6.057
6.389
165
55
⫺0.146
5.18
0.144
4.101
4.316
4.703
5.180
5.714
6.250
6.597
170
89
⫺0.147
5.316
0.145
4.203
4.424
4.823
5.316
5.867
6.419
6.779
175
93
⫺0.144
5.426
0.145
4.293
4.518
4.924
5.426
5.988
6.551
6.916
180
65
⫺0.139
5.510
0.144
4.363
4.591
5.002
5.510
6.077
6.645
7.013
185
27
⫺0.134
5.575
0.143
4.421
4.650
5.064
5.575
6.144
6.714
7.083
190
7
⫺0.127
5.632
0.142
4.475
4.706
5.121
5.632
6.202
6.770
7.138
195
5
⫺0.121
5.685
0.140
4.528
4.759
5.175
5.685
6.253
6.818
Age, y
Males
Females
Height, cm
Males
7.184
(Continued)
Reusz et al
Table 2.
Variable
221
Continued
N
L
M
S
5th
10th
25th
50th
75th
90th
95th
Females
115
5
1.854
4.286
0.130
3.268
3.515
3.897
4.286
4.648
4.954
5.130
120
13
1.706
4.340
0.129
3.334
3.573
3.950
4.340
4.707
5.021
5.203
125
13
1.521
4.415
0.129
3.420
3.651
4.023
4.415
4.790
5.115
5.305
130
17
1.337
4.506
0.129
3.514
3.741
4.110
4.506
4.892
5.230
5.429
135
15
1.153
4.609
0.129
3.614
3.837
4.205
4.609
5.007
5.361
5.571
140
13
0.971
4.713
0.130
3.711
3.932
4.301
4.713
5.126
5.499
5.722
145
20
0.790
4.821
0.131
3.806
4.026
4.399
4.821
5.251
5.645
5.883
150
24
0.611
4.923
0.132
3.898
4.116
4.492
4.923
5.371
5.786
6.040
155
50
0.435
5.015
0.133
3.987
4.202
4.577
5.015
5.476
5.910
6.178
160
133
0.261
5.106
0.133
4.079
4.291
4.664
5.106
5.578
6.030
6.312
165
121
0.091
5.212
0.133
4.179
4.390
4.763
5.212
5.699
6.172
6.472
170
63
⫺0.078
5.328
0.134
4.283
4.493
4.870
5.328
5.834
6.333
6.654
175
22
⫺0.246
5.446
0.135
4.391
4.600
4.979
5.446
5.970
6.496
6.838
180
4
⫺0.414
5.564
0.135
4.499
4.707
5.088
5.564
6.105
6.658
7.023
L indicates skewness; M, median; S, coefficient of variation. The coefficient of variation (CV) is calculated as CV⫽(SD/M).
Conversely, there was a negative correlation between HR and
PWV (r⫽⫺0.08; P⫽0.02). By multiple regression analysis,
age, height, and MAP (ß⫽0.21, 0.16, and 0.24 and SE of
ß⫽0.04, 0.039, and 0.029, respectively; P⬍0.001) proved to
be major determinants of PWV in the healthy population (see
also Table S2). Accordingly, PWV of healthy children may
be expressed as follows:
PWV (m/s)⫽0.049⫻age ( years)⫹0.008⫻height (cm)
⫹0.024⫻MAP (mm Hg)⫹1.129
(3)
Standardized PWV Data
PWV of the 1008 subjects showed a nonnormal distribution
(P⬍0.001). The fitting procedure used for the transformation
of the raw PWV data to Z scores resulted in a PWV zage of
0.0008 (0.995; mean [SD]) and a PWV zheight of 0.0901
(1.024), indicating effective normalization of the skewed
original data. LMS values were determined and percentile
boundaries were calculated and plotted according to sex, age,
and height (Table 2 and Figure).
A close correlation was found between PWV Z for age and
for height (r⫽0.95; P⫽0.00001; see Figure S1). An individual example for the practical use of tables and data transformation is provided in the online Data Supplement.
Discussion
This study is the first report to provide novel, distributionindependent age- and height-specific reference values for
PWV in children and teenagers, making them suitable for the
evaluation and follow-up of subgroups in the pediatric population who are at risk for long-term cardiovascular disease.
PWV is a powerful parameter of arterial stiffness. It is a
surrogate marker for cardiovascular events in adult hypertension and end-stage renal disease20 –22; however, such data in
children are sparse.5,10 –12,23 The difficulty of using PWV in
children is its dependence on age and body dimensions.10 –12
To evaluate the data obtained in different pediatric patient
groups, the current practice is to use either controls matched
for age or to use age-specific normal values established in
large healthy populations. Unfortunately, the problem of
growth deficit is not solved by this approach.
In a study of children on hemodialysis, Covic et al23 found
an increase in PWV compared with healthy controls. However, in their study, age- and height-matched controls were
used for comparison purposes. In our previous study aimed at
confirming the increase in PWV in children on dialysis, to our
surprise we only noted a weak (not significant) tendency
toward an increase in PWV in dialysis patients. Because of
the growth deficit caused by end-stage renal disease, we
were, therefore, compelled to use a control group matched for
age and height to demonstrate the true magnitude of PWV
differences.11 In a subsequent step, normative PWV values
were established with the use of arithmetic mean and SD of
a database of 188 healthy children.12
In the present work, using an extended database of 1008
healthy children and young adults, we applied the LMS
method, which takes into account asymmetrical data distribution to create reference tables for sex-, age-, and heightspecific percentiles of PWV in healthy children and teenagers. This type of approach is widely accepted in pediatrics.
The universally used reference tables for 24-hour BP monitoring, as well as normative values for intima-media thickness
in healthy adolescents, are based on this very principle.24,25
Herein, analysis of the raw PWV data revealed a nonGaussian distribution across the entire pediatric age range.
The skewed distribution invalidates the calculation of conventional SD (Z) scores based on arithmetic means and SDs.
On the other hand, the LMS method is a technique accounting
for any degree and direction of distribution skewness of the
sample. It has the advantage of not requiring grouping of the
Hypertension
A
August 2010
8
Males
PWV (m/s)
7
75th
6
50th
25th
10th
5th
5
0
5
10
15
Age (years)
50th
25th
10th
5th
5
0
5
75th
50th
5
25th
10th
5th
10
15
Age (years)
20
Females
8
95th
90th
6
7
95th
90th
6
75th
5
50th
25th
10th
5th
4
4
3
100
3
20
Males
8
7
PWV (m/s)
6
4
PWV (m/s)
B
95th
90th
75th
7
4
3
Females
8
95th
90th
PWV (m/s)
222
120
140
160
180
200
3
100
120
Height (cm)
140
160
180
Height (cm)
Figure. PWV percentile curves according to age (A) and height (B).
covariate for the calculation of percentiles, thereby avoiding
the distorting effects of variable cell sizes. Hence, the L, M,
and S reference values published herein are readily applicable
for defining PWV abnormalities independent of age and of
body size in diseased pediatric populations, and the statistical
basis of the PWV percentiles provided here is superior to our
previous estimates.12
The growth pattern and final height of the sexes differ
significantly. Thus, although the PWV of age- and heightmatched boys and girls is similar, as shown herein and in our
previous studies,11,12 the PWVs of boys and girls did differ
significantly in the third and fourth age quartiles, that is, after
puberty. This is because the girls matched to the boys are
taller than the average of their age group; in other words, they
have a higher height SDS. Hence, because of the difference in
height development between sexes, the normalized data
obtained for boys and girls are presented separately.
The positive correlation of PWV with age, body dimension, and BP, as well as the negative correlation with HR, was
confirmed in this large population of healthy children.11,12
However, according to the results of the multivariate regression analysis, only age, height, and MAP were independent
predictors of PWV. Based on the multivariate model, a
general equation can be generated allowing for the prediction
of the PWV value, as shown in the Results section. The use
of MAP herein was justified by previous data showing that,
aside from age, MAP (but not SBP or DBP) proved to be an
independent predictor of carotid-femoral PWV.26
The reference tables generated herein expressing PWV
according to height are presented for use in pediatric populations with growth deficit. The close relationship between
PWV Z for age and PWV Z for height values indicates that
smaller children have lower PWV values, whereas taller
children have higher PWV values in any given age group.
Evidence that the dimensions and elastic properties of the
arterial tree are intimately related to height was initially
provided by morphological studies.27,28 The physiological
basis of the influence of body size on arterial wall properties
was further demonstrated by Senzaki et al,29 who established
reference ranges for age-associated changes in arterial pulsatile properties in 112 pediatric patients after cardiac catherization. Their results indicated a progressive increase in
arterial compliance despite a decrease in arterial wall elasticity, leading to the conclusion that the increase in arterial size
accompanying increased body size outweighs the effects of
age on intrinsic elastic properties of arterial walls. Such
influence of body dimensions on the elastic properties of
arteries was also demonstrated in a more recent study by
Jourdan et al,25 in which both intima-media thickness and
arterial stiffness were shown to change with age and body
size. Accordingly, the authors concluded that morphological
and functional measurements of large arteries should be
normalized to take into account changes during adolescence.
Conclusions
The reference tables provided in this multicenter study
involving a cohort of ⬎1000 children and teenagers are based
on the LMS method, which enables the calculation of
appropriate SDS values of PWV in children and teenagers.
Age, height, and MAP were found to be the major determinants of PWV, hence emphasizing the need to consider height
in patients with growth deficits.
Perspectives
This study is the first report to provide distributionindependent age and height-specific reference values for
Reusz et al
PWV in children and teenagers. Because the traditional end
points such as stroke, myocardial infarction, and mortality
used in adult studies are unsuitable to evaluate the risks or
benefit of a given intervention or treatment in pediatric
clinical trials, surrogate markers are needed in pediatric
studies. Examples for such studies are the use of angiotensin-converting enzyme inhibitors or angiotensin receptor
blockers in hypertensive and/or diabetic children and the use
of calcium-free phosphate binders in uremic children. PWV is
a sensitive marker of arterial stiffness and, consequently, of
cardiovascular outcome in adults.1,2,4
Our reference values in children represent a suitable tool
for use in such longitudinal interventional studies, as suggested by the recent scientific statement of the American
Heart Association.5 However the final evaluation and validation of the method to establish the cutoff points for predicting
hard cardiovascular end points could only be provided by the
follow-up of children growing into adulthood.
Limitations
A complete analysis of all of the risk factors potentially
influencing PWV in this healthy, young population was
beyond the scope of the original goals of this study. We did
perform a subgroup analysis in 300 healthy children and
could not establish any correlation among (normal) glucose
and lipid values and PWV. Because the analysis was not
complete, we did not include all of these results into the
article. For follow-up studies in populations with increased
cardiovascular risk, it will be necessary to include these data
in the analysis.
Sexual steroids could influence the elastic properties of the
arteries in women. We did not address this question in the
present study; however, in their work, Robb et al30 could
show that PWV is not significantly affected by the periodical
physiological changes of steroid hormones in women.
Acknowledgments
We thank Prof Dea Campana, the headmistress of the “Liceo
Scientifico Augusto Righi” high school in Cesena; Prof Marino
Mengozzi, the assistant headmaster, and Veronika Hámori and Dr
Mária Papp, the directresses of the “Fazekas” and “Raoul Wallenberg” high schools, respectively; Kolos Kovács, the directress of the
“János Arany Primary and High School”; and László Bodó, the
director of the “Bakáts Square Primary School” in Budapest, as well
as the staff and all of the students for their invaluable cooperation.
We are particularly grateful to Prof Giovanna Amaduzzi, Filomena
Racioppa, and Maurizio Marchetti, who made the research in Italy
possible. We also thank the nursing staff of the Department of
Internal Medicine of the Bufalini Hospital in Cesena and of the First
Department of Pediatrics at the Semmelweis University in Budapest
for contributing to the realization of this study. In addition, we thank
Dr Ali Belkheir, public health director of Ghardaia; Bachir Bahaz,
director of the Metlili Hospital; the medical team of Metlili and
Ghardaia; the staff and the students of Metlili High School; and
Fatma Chehima for secretarial service.
Sources of Funding
The study was supported by grants OTKA-071730 (Országos Támogatási Kutatási Alap [National Scientific Research Fund]; to
G.S.R.), TÁMOP-4.2.2-08/1/KMR-2008-0004 (Társadalmi Megújulás Operativ Program [Social Renewal Operational Program]; to
A.J.S.), and ETT 06-123/2009 (Egészségügyi Tudományos Tanács
[Academic Health Council Grant]; to G.S.R.).
223
Disclosures
P.S. is a consultant for PulsePen (DiaTecne srl, Milan, Italy).
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ONLINE DATA SUPPLEMENT
REFERENCE VALUES OF PULSE WAVE VELOCITY IN HEALTHY CHILDREN AND
TEENAGERS
SHORT TITLE:
Reference values of pulse wave velocity
AUTHORS:
George S Reusz1, Orsolya Cseprekal1, Mohamed Temmar2, Éva Kis1, Abdelghani Bachir
Cherif3, Abddelhalim Thaleb3, Andrea Fekete1, Attila J Szabó1, Athanase Benetos4, Paolo
Salvi4,5
1
Ist Department of Pediatrics, Semmelweis University, Budapest, Hungary
2
Telomere Cardiology Centre Ghardaia, Algeria
3
Department of Internal Medicine University of Blida, Algeria
4
Department of Internal Medicine and Geriatrics, INSERM U961, University of Nancy,
France.
5
Department of Internal Medicine, University of Bologna, Italy.
George S. Reusz and Orsolya Cseprekal contributed equally to the manuscript
CORRESPONDING AUTHOR:
György S. Reusz MD. PhD
First Department of Pediatrics
Semmelweis University
Budapest, Str. Bókay János 53-54. H-1083
Phone: 36-30-9869545
Fax: 36-1-3247795
E-mail: [email protected]
1
Table S1. Comparison of 132 children matched for gender, age and height
Anthropometric data
N
Age (years)
Weight (kg)
Weight SDS *
Height
Height SDS *
BMI (kg/m2)
BMI SDS
SBP (mmHg)
SBP SDS
DBP (mmHg) *
DBP SDS *
MAP (mmHg)
HR (1/min)
PWV (m/s)
Males
66
14.3 (13.5-15.2)
52.1 (48.7-55.7)
0.034 (-0.140-0.208)
161.4 (157.7-165.3)
-0.081 (-0.249-0.086)
19.5 (18.8-20.3)
0.047 (-0.138-0.231)
114.6 (111.5-117.7)
0.407 (0.208-0.606)
64.0 (62.3-65.8)
0.032 (-0.112-0.176)
80.9 (79.0-82.8)
76 (72-79)
4.916 (3.106-7.396)
Females
66
14.4 (13.6-15.2)
52.5 (48.7-56.5)
0.353 (0.188-0.518)
161.3 (157.4-165.3)
0.962 (0.762-1.161)
19.6 (18.8-20.5)
0.061 (-0.089-0.212)
112.0 (109.5-114.3)
0.255 (0.049-0.460)
67.4 (65.7-69.1)
0.246 (0.086-0.405)
82.2 (80.5-84.0)
76 (74-79)
5.075 (3.790-6.606)
*
p<0.05 males vs. females.
Data are shown as mean and 95% confidence interval (CI) except for PWV, which is
expressed as median (range). N indicates Number of subjects; BMI, body mass index; SBP,
systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; HR,
heart rate; SDS, standard deviation score; PWV, pulse wave velocity.
2
Table S2. Results of uni- and multivariate regression analysis
PWV (m/s)
Variables
Age (years)
Height (m)
MAP
(mmHg)
Weight (kg)
SBP (mmHg)
DBP (mmHg)
BMI (kg/m2)
HR (1/min)
Univariate regression
analysis
Multiple regression analysis
0.21
0.16
St error of
ß
0.04
0.039
0.00001
0.00007
0.24
0.029
0.00001
r
p<
ß
0.47
0.44
0.00001
0.00001
0.43
0.00001
0.39
0.43
0.33
0.21
-0.08
0.00001
0.00001
0.00001
0.00001
0.02
p<
NS
NS
NS
NS
NS
r and ß indicate respective correlation coefficients; p, level of significance; SBP, systolic
blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; BMI, body mass
index; HR, heart rate.
3
Figure S1. Correlation between PWV Zage and PWV Zheight
4
3
PWV Zheight
2
1
0
-1
-2
-3
-4
-3
-2
-1
0
1
2
3
4
PWV Zage
r = 0.95; p = 0.00001
PWV Zheight = 0.1+0.96*x
4
An example of the conversion of raw data to normalized values
To calculate the PWV Z score for height of a 13.7 year-old boy, 132 cm tall, with a growth
deficit and a PWV value of 5.765 m/s:
A: First select the closest raw value of Table 4 ba relative to the child’s height (130 cm)
B: Select the L, M, and S values from row 130 cm
L= -0.089, M=4.378, S=0.126
C: These parameters are then used by the statistical software based on the equation
SDS=[(Y/M(t))L(t)-1]/(L(t) x S(t))
where Y is the child’s individual PWV parameter and L(t), M(t), S(t) are the specific values
of L, M and S, respectively.
PWV Z height = 2.158
Thus, in this particular case, the PWV is more than 2 standard deviations higher compared to
the healthy population.
5
Center effect, ethnic differences
There was no significant difference between the PWV data from the different regions
compared within the age quartiles. This means that Hungarian, Italian and Arabic, children of
the same age had similar (average) PWV values.
In particular, PWV of the gender and age-matched Arabic and Italian boys was 5.602 (0.761)
and 5.336 (0.691) respectively (P=NS), and those of gender- and age-matched Hungarian and
Italian subjects 5.693 (0.852) and 5.354 (0.865) respectively (p=NS). Consequently, this
means that there were no significant ethnic differences between the populations studied.
6

George S. Reusz, Orsolya Cseprekal, Mohamed Temmar, Éva Kis, Abdelghani... Abddelhalim Thaleb, Andrea Fekete, Attila J. Szabó, Athanase Benetos and... Reference Values of Pulse Wave Velocity in Healthy Children and...

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