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Is arterial stiffness predicted by continuous metabolic syndrome score in obese children?
Journal of the American Society of Hypertension
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Research Article
Is arterial stiffness predicted by continuous metabolic syndrome score in obese children? Katarina Prochotska, MDa,*, Laszlo Kovacs, MD, PhDa, Eva Vitariusova, MD, PhDa, and Janusz Feber, MD, FRCPCb a nd
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Department of Pediatrics, Comenius University Medical School, University Children’s Hospital, Bratislava, Slovakia; and b Division of Nephrology, Department of Pediatrics, Children’s Hospital of Eastern Ontario, Ottawa, Canada Manuscript received June 18, 2015 and accepted October 26, 2015
Abstract The aim of the article was to evaluate arterial stiffness, an early marker of increased cardiovascular risk, in relation to obesity. The continuous metabolic syndrome (cMetS) score was calculated as sum of Z score of mean arterial pressure, body mass index, serum glucose, triglyceride, and high-density lipoprotein cholesterol in 144 obese patients and 66 nonobese controls. Ambulatory arterial stiffness index (AASI) was calculated as 1 minus regression slope of diastolic on systolic blood pressure from ambulatory blood pressure measurements. The mean AASI increased progressively with severity of obesity. The receiver operator curve analysis of body mass index and AASI showed area under the curve of 0.64 0.06; cMetS area under the curve was 0.72 0.05 suggesting a better predictive power of the cMetS for an increased AASI (>0.3). Patients with obesity have significantly higher arterial stiffness. A composite score such as cMetS seems to be better predictor of an increased stiffness than individual risk factors. J Am Soc Hypertens 2015;-(-):1–8. Ó 2015 American Society of Hypertension. All rights reserved. Keywords: Arterial stiffness index; continuous metabolic syndrome; hypertension; obesity.
Introduction Atherosclerotic arterial lesions1 and arterial wall noncompliance2 can already be present in children and adolescents. The most relevant cardiovascular risk factors favoring atherogenic development are overweight and obesity.3 As changes in arterial function precede the development of atherosclerotic lesions,4 an increased arterial stiffness may be an early marker of increased cardiovascular risk,5 especially in obese patients with metabolic syndrome (MS). Across a wide age range, the diameter
Funding: This work was supported by Ministry of Health of the Slovak Republic under the project registration number 2012/5-UKBA-5, APVV-14-0234. Conflict of interest: Authors have no conflicts of interest to disclose. *Corresponding author: Katarina Prochotska, MD, 2nd Department of Pediatrics, Comenius University Medical School, University Children’s Hospital, Limbova 1, Bratislava 83340, Slovakia. Tel: (00421) 910-992-392; fax: (00421) 59-357-104. E-mail: [email protected]
and stiffness of arteries increase in proportion to the body mass index (BMI).6,7 The direct measurement of arterial stiffness by pulse wave velocity (PWV) is limited in children due to agerelated technical difficulties, fear from additional testing, and so forth. The arterial stiffness can be indirectly assessed by calculating the ambulatory arterial stiffness index (AASI)8 from ambulatory blood pressure monitoring (ABPM). AASI has been shown to strongly correlate with direct measures of arterial stiffness, such as PWV and augmentation index, and to provide prognostic information on cardiovascular mortality in adults.5,8 Studies have also shown that this index is associated with preclinical target organ damage in hypertensive adults.9,10 The literature data on AASI in children are scant and rather controversial. Simonetti et al found an increased AASI in hypertensive children regardless of their BMI,11 whereas Stergiou et al12 reported a strong correlation between AASI and BMI but no change of AASI with hypertension. Both these studies included children and adolescents with a relatively normal or slightly increased BMI without severe obesity. Recently, an increased AASI was
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also reported in obese children compared with nonobese controls.13 Children with severe obesity or MS are even more likely to have an increased arterial stiffness. The goal of our study was therefore to evaluate AASI in children with various degrees of obesity including severe obesity and MS in comparison with healthy controls. We also sought to analyze various risk factors for increased AASI, in particular the impact of ambulatory hypertension and MS.
Patients and Methods Patients We retrospectively analyzed all children and adolescents who were consecutively referred by community physicians to our center from May 2005 to September 2011 for assessment of obesity (BMI 2 standard deviation score [SDS]). None of patients experienced known chronic illness, and none received antihypertensive medications at the time of assessment. Patients with secondary forms of obesity were excluded. Control group recruited from children, who were referred for assessment of chronic illness such as growth retardation, chronic headache, chronic abdominal pain, and so forth. None of these children received any medication at the time of assessment, and none had any previous blood pressure (BP) problems. All enrolled patients had their weight, height, and BP measured, underwent ABPM, and had blood drawn for serum creatinine, serum uric acid, fasting cholesterol, triglycerides, and glucose levels; 24-hour urine collection was analyzed for albumin excretion. One hundred forty-four obese patients and 66 nonobese controls were included in the final analysis.
Anthropometry Weight and height were measured with a digital device (TONAVA TH200, Tonava a.s., Czech republic). BMI was calculated as body weight in kilograms divided by the square root of height in meter; absolute height and BMI values were subsequently transformed into SDS based on normative values obtained from Slovak children population.14
Laboratory Measurements Serum creatinine, uric acid, total cholesterol, highdensity lipoprotein (HDL) cholesterol, triglycerides, glucose levels, and urine albumin were measured by a Cobas Integra 800, Roche device. Non-HDL cholesterol was calculated as total cholesterol minus HDL cholesterol. The glomerular filtration rate was estimated using serum creatinine and height using the Schwartz formula.15 Obesity was defined as BMI equal or above 2.00 SDS.
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Metabolic Syndrome MS was defined in two different ways: Traditional metabolic syndrome definition according to the International Diabetes Federation criteria16 with two exceptions: (1) waist circumference was replaced with BMI Z score (BMI 2.0) and (2) office hypertension was replaced by ambulatory hypertension defined in the paragraph below. Continuous metabolic syndrome (cMetS) was calculated as a sum of the scores17,18: BMI Z score, mean arterial pressure (MAP) 24-hour Z score (from ABPM), Z scores for HDL cholesterol, triglycerides, and glucose levels were derived from the mean and standard deviation (SD) of our obese patients; the HDL cholesterol Z score was multiplied by 1 (inverted parameter for cardiovascular risk).
Classification of Obese Patients Obese patients were divided into three groups based on their BMI Z scores percentiles (33 percentile ¼ BMI Z score 3.5, 66% ¼ BMI Z score 4.5) obtained from the frequency distribution of BMI Z scores: BMI 1 ¼ patients with BMI Z scores 2.00 and <3.49. BMI 2 ¼ patients with BMI Z scores 3.50 and <4.49. BMI 3 ¼ patients with BMI Z scores >4.50.
Office Blood Pressure Office blood pressure (OBP) was measured only once on the day of the ABPM using a sphygmomanometer device (Tonometer 40, Chirana 400, Slovak Republic) on a nondominant arm with an appropriate cuff size. Obtained values in mm Hg were subsequently transformed into BP Z scores using the normative values.19
ABPM ABPM was measured using the validated oscillometric monitor Meditech-04 (SunTech Medical Instruments, Inc, USA). A cuff of an appropriate size was placed on a nondominant arm as per current guidelines. We defined the nighttime period as the time between midnight and 6 AM; the daytime period was defined as 8 AM to 8 PM. The obtained and verified ABPM results were subsequently imported into Chronos-Fit software (P. Zuther. S. Gorbey and B. Lemmer. Chronos-Fit 1.06. http://www.umm.uniheidelberg.de/inst/phar/lehre/chrono.html 2009). The following parameters were analyzed: average systolic (SBP) and diastolic blood pressure (DBP), MAP, their respective loads, and day-to-night differences. All these parameters were calculated for 24-hour, daytime, and nighttime periods separately. Absolute BP values were subsequently transformed into Z scores based on ABPM normative values.20 Blood pressure load (BPL) was defined as the number of BP values exceeding the 95th percentile of a given BP (SBP, DBP, and MAP) at a given period (24
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hour, daytime, and nighttime). The highest BPL obtained (either SBP or DPB or MAP) at any period was then defined as the maximum BPL and used for further analysis. For classification of ambulatory hypertension, we only used the ambulatory BP results regardless of the office BP. Our classification was as follows: Patients were considered to have ambulatory normotension if their 24-hour daytime and nighttime average SBP, DBP, and MAP were below the 95th percentile. If one or more of the BP parameters (either SBP or DBP or MAP) were above the 95th percentile at any period, patients were considered to have ambulatory hypertension regardless of their BP load. Patients were further classified as nondippers if their day-to-night BP (D/N BP) difference was below 10% (either SBP or DBP or MAP).
Ambulatory Arterial Stiffness Index From 24-hour ABPM recordings of each patient, the individual regression slopes of diastolic on SBPs were calculated.8 AASI was then defined as 1 minus the regression slope. Based on Simonetti et al, an AASI equal or higher than 0.3 was considered as elevated.11
Statistical Analysis Data are shown as mean SD if normally distributed or the median and interquartile range (25th and 75th percentile) in cases of abnormal distribution. Normal/abnormal distribution of variables was tested with the D’Agostino and Pearson omnibus normality test. Continuous variables in patient groups were compared using the analysis of variance with Tukey correction for multiple comparisons (normally distributed data) or the Kruskal–Wallis test with Dunn’s multiple comparison correction (not normally distributed data). Categorical variables (proportion of patients between groups) were compared using a Fisher test or a chi-square test. The difference in proportions was analyzed using the chi-square test for trend. Multiple regression analysis (Spearman correlation) was performed to analyze correlations between AASI and various clinical and BP parameters. Factor identified as statistically significant in the univariate analysis was subsequently included in the multivariate analysis. Linear regression was used to evaluate association between cMetS and AASI. Receiver operator curves were constructed to analyze whether BMI SDS and cMetS can predict an increased AASI defined as AASI higher than 0.3. Results were considered statistically significant if the P-value was below .05. Most statistical analyses were performed with the GraphPad Prism software, version 5.0 (GraphPad Software, La Jolla, CA, USA). The multivariate analysis was performed with Stata software (StataCorp LP, TX, USA). The study was approved by local ethics committee.
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Results Characteristics of Study Population The demographic data are shown in Table 1. The mean SD age of all patients was 14.7 2.9 years and did not differ between the groups. The study groups (obese patients with various degrees of obesity) and control group did not differ regarding gender distribution, kidney function, and urine albumin and serum glucose. Children in all obese groups had a higher BMI compared with the control group; obese groups differed in their BMI as per definition of groups. Obese patients with the highest degree of obesity (BMI 3) were significantly taller than controls. Obese patients in groups BMI 2 and BMI 3 had a higher serum uric acid and a lower HDL cholesterol than controls. Total cholesterol and triglyceride levels were higher in all three groups of obese patients; non-HDL cholesterol was significantly higher only in BMI 2 group. The percentage of patients with traditionally defined MS (traditional metabolic syndrome) was 39.6%, 54.2%, and 64.6% in BMI 1, BMI 2, and BMI 3, respectively (chisquare for trend, P ¼ .014). Similarly, the mean cMetS score increased progressively from 2.40 to 6.31 and from BMI 1 to BMI 3 groups (posttest for linear trend, r2 ¼ 0.27; P < .0001).
Blood Pressure OBP (systolic and diastolic) was significantly higher in all obese groups than in control group, but there was no difference in OBP between obese patients (Table 2). By definition, all ABPM parameters except for nighttime dipping were significantly higher in obese patients with ambulatory hypertension as compared with controls and obese patients without hypertension (Table 2). Overall, the ambulatory hypertension was found in 82 of 144 obese children (57%). Children with obesity had significantly lower D/N DBP and D/N MAP ratio, but D/N SBP ratio was not different among groups. The nondipping status was observed in 32 (52%) obese nonhypertensive patients and in 46 (56%) obese hypertensive children. Max BP load was significantly elevated in all obese patients compared with controls (Table 2).
Ambulatory Arterial Stiffness Index AASI results are shown in Table 2 and Figure 1. The mean SD AASI increased progressively from 0.35 0.17 in the control group to 0.40 0.16 in BMI 1, 0.44 0.14 in BMI 2, and 0.47 0.15 in BMI 3 (posttest for linear trend, r2 ¼ 0.084; P < .0001). From all parameters indicated in Tables 1 and 2, the AASI significantly correlated (multiple correlation) only with height SDS (r2 ¼ 0.22; P ¼ .009), serum cholesterol (r2 ¼ 0.23; P ¼ .007), D/N MAP (r2 ¼ 0.17; P ¼ .04),
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Table 1 Demographic and laboratory data Parameter
Control
Group BMI 1
Group BMI 2
Group BMI 3
P-Value
Number of patients Sex (M:F) Age (y) BMI (kg/m2) BMI (SDS) Height (cm) Height (SDS) Fasting glycemia Serum creatinine (mmol/L) Serum uric acid GFR (mL/min/1.73 m2) Urine albumin Serum cholesterol S HDL chol Serum triglycerides tMetS cMetS
66 35/31 15.09 (12.90, 16.47) 19.77 (17.31, 23.76) 0.06 (0.58, 1.19) 162.8 (150, 172.1) 0.66 (0.19, 1.22) 4.5 (4.2, 4.9) 62.28 13.84 274.5 (228.3, 313.3) 97.21 (84.91, 107.5) 6.92 (4.88, 57.83) 3.74 (3.3, 4.17) 1.38 (1.10, 1.72) 0.82 (0.69, 1.01)
48 26/22 14.12 (11.95, 16.17) 28.43*,y,z (26.92, 29.93) 3.04*,y,z (2.57, 3.23) 162 (154.3, 172.5) 0.93 (0.06, 1.54) 4.3 (3.8, 5.1) 61.39 14.61 318.5z (261.5, 368.5) 99.01 (81.63, 111.8) 6.85 (4.25, 12.15) 4.15* (3.75, 4.72) 1.13 (1.03, 1.25) 1.19* (0.85, 1.40) 39.58% 2.36 2.28y,z
48 31/17 14.7 (12.1, 17.05) 32.1*,z (31.13, 32.92) 4.00*,z (3.77, 4.21) 167 (155.3, 175.8) 1.33 (0.52, 1.82) 4.5 (3.9, 4.9) 61.00 12.13 349* (300.5, 402.5) 97.79 (86.53, 108.1) 7.62 (4.76, 13.81) 4.36* (3.86, 5.03) 0.97* (0.86, 1.17) 1.37* (1.07, 1.90) 54.17% 4.52 2.77z
48 31/17 14.74 (12.13, 16.59) 35.99* (33.62, 37.7) 5.24* (4.91, 5.88) 170* (163, 177.8) 1.52* (0.53, 2.65) 4.4 (4, 5) 63.78 11.61 419* (352, 480.5) 99.99 (84.75, 107.6) 7.62 (4.72, 21.25) 4.28* (3.77, 4.95) 1.00* (0.88, 1.2) 1.38* (0.98, 2.08) 64.58% 6.31 2.77
.13 .84 <.0001 <.0001 .019 .002 .006 .75 <.0001 .90 .52 <.0001 <.001 <.0001 .01 <.0001
BMI, body mass index; cMetS, continuous metabolic syndrome; GFR, glomerular filtration rate; SDS, standard deviation score; S HDL chol, serum high-density lipoprotein cholesterol; tMetS, traditional metabolic syndrome. Values are given as mean standard deviation (if normally distributed) or median and interquartile range (if not normally distributed). * Significantly different from control group (P < .05). y Significantly different from group BMI 2 (P < .05). z Significantly different from group BMI 3 (P < .05).
and the cMetS (r2 ¼ 0.21; P ¼ .01; Figure 2). Only borderline correlation was found between BMI SDS and AASI (r2 ¼ 0.16; P ¼ .059). However, on the multivariate analysis, none of these parameters correlated significantly with the AASI.
Further analysis of the impact of obesity on arterial stiffness using the receiver operator curve analysis of BMI and AASI showed area under the curve of 0.64 0.06; the cMetS area under the curve was 0.72 0.05 suggesting a better predictive power of the cMetS for an increased
Table 2 Office and 24-hour ambulatory blood pressure values (Z scores), blood pressure load (%), and ambulatory arterial stiffness index Parameter
Control
Group BMI 1
Group BMI 2
Group BMI 3
P-Value
Office SBP Office DBP MAP day MAP night MAP 24 hour SBP day SBP night SBP 24 hour DBP day DBP night DBP 24 hour BP load max D/N SBP D/N MAP D/N DBP AASI
0.26 (0.19, 0.75) 0.29 (0.04, 0.79) 0.61 (2.03, 0.61) 1.32 (2.30, 0.04) 1.25 (2.29, 0.41) 0.20 (1.29, 0.80) 0.45 (0.57, 1.12) 0.17 (0.94, 0.81) 0.38 (1.31, 0.34) 0.43 (0.68, 1.38) 0.35 (1.25, 0.55) 18.02 (7.96, 23.31) 1.12 (1.07, 1.17) 1.25 (1.16, 1.34) 1.23 (1.14, 1.30) 0.35 0.17
0.99* (0.42, 1.85) 1.34* (0.47, 1.55) 0.31 (1.02, 0.49) 0.47* (0.58, 1.10)* 0.01* (0.81, 0.79) 0.61 (0.21, 1.34) 1.04* (0.43, 2.07) 0.89* (0.04, 1.64) 0.16 (1.01, 1.25) 0.77* (0.11, 2.35) 0.37* (0.57, 1.78) 46.32* (23.66, 79.79) 1.11 (1.05, 1.16) 1.16* (1.09, 1.21) 1.18 (1.09, 1.25) 0.39 0.16
1.15* (0.55, 1.88) 0.72* (0.32, 1.46) 0.03 (0.81, 0.65) 0.80* (0.53, 1.66) 0.32* (0.51, 1.21) 0.42 (0.04, 1.29) 1.17* (0.54, 1.97) 0.78* (0.17, 1.75) 0.35 (1.24, 0.43) 0.54* (0.36, 1.14) 0.02 (0.87, 0.72) 55.05* (24.11, 68.13) 1.10 (1.06, 1.14) 1.13* (1.09, 1.18) 1.15 (1.11, 1.22) 0.44 0.14*
1.08* (0.64, 1.88) 1.28* (0.71, 1.75) 0.03 (0.94, 1.18) 1.20* (0.16, 2.17) 0.46* (0.46, 1.60) 0.86* (0.36, 1.83) 1.82* (1.00, 2.30) 1.26* (0.62, 2.35) 0.24 (1.45, 0.65) 0.90* (0.33, 1.86) 0.27 (0.89, 1.31) 60.00* (30.45, 83.33) 1.08 (1.04, 1.15) 1.12* (1.06, 1.21) 1.14* (1.06, 1.25) 0.47 0.15*
<.0001 <.0001 .09 <.0001 <.0001 <.001 <.0001 <.0001 .18 .004 .010 <.0001 .11 <.0001 .03 <.001
AASI, ambulatory arterial stiffness index; BMI, body mass index; BP load max, highest blood pressure load in SBP or DBP or MAP; DBP, diastolic blood pressure; D/N, day-to-night ratio; MAP, mean arterial pressure; SBP, systolic blood pressure. * Significantly different from control group (P < .05).
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Figure 1. AASI in relation to the severity of obesity. BMI 1 ¼ patients with BMI Z scores 2.00 and <3.49, BMI 2 ¼ patients with BMI Z scores 3.50 and <4.49, and BMI 3 ¼ patients with BMI Z scores >4.50. AASI, ambulatory arterial stiffness index; BMI, body mass index.
AASI (>0.3; Figure 3). The optimal predicting value of an increased AASI above 0.3 was the cMetS score of 4.567 with 55% sensitivity and 90% sensitivity (Figure 3).
Discussion The main findings of our study are (1) AASI is significantly elevated in children with higher degrees of obesity (BMI SDS > 3.5) compared with normal population and (2) a cMetS score correlated significantly with AASI and was a better predictor for an increased AASI than BMI SDS.
Figure 2. Correlation between AASI and cMetS. AASI, ambulatory arterial stiffness index; cMetS, continuous metabolic syndrome.
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An increased arterial stiffness in children has already been reported,21 as well as its relation to BMI3,7,22–24 and/or hypertension.25 In these studies, authors used a direct assessment of arterial stiffness by PWV assessment2,7,24,25 or a lumen diameter changes from the ultrasound tracing during intima media thickness measurement.3,21–23,26 We used ABPM to calculate the AASI, which is an indirect assessment of arterial stiffness and was shown to correlate with arterial stiffness measured by PWV.8 In addition, the AASI was found to be a good marker of cardiovascular risk in the population.5 There are only a limited number of studies using AASI for assessment of arterial stiffness in pediatric patients.11,12,27 Simonetti11 found an increased AASI in hypertensive children (mostly secondary hypertension) compared with normotensive children, but no significant correlation between AASI and BMI was detected. However, only 6% of hypertensive patients were obese and most patients (71%) experienced secondary hypertension.11 We also found only borderline correlation between AASI and BMI Z score, despite the fact that our children experienced significant obesity and more than 50% had MS. In contrast, Stergiou et al showed that AASI correlates with the BMI, but no difference was noted in AASI between hypertensive and normotensive children. However, in this study,12 only absolute BMI values in kg/m2 and BP values in mm Hg were used for the correlation analysis. This approach may potentially lead to skewed results in children of various age and height. We used ambulatory BP Z scores for the correlation analysis and did not find any significant correlation between AASI and any of the 24hour BP variables (expressed in Z scores) except for the nighttime dipping. In a recent study by Saner et al,13 the AASI was significantly higher in obese children compared with a matched lean control group. Interestingly, the AASI was influenced by BMI and pulse pressure, independently of BP values.13 As the BMI and BP may be interrelated, especially in obese patients, it is rather difficult to assess individual contributions of BP and BMI on the results of AASI. Saner et al13 suggest that there might be other factors involved in increased arterial stiffness such as sympathetic nervous system, leptin, oxidative stress, low vitamin D, and so forth. The traditional definition of obesity relies on the assessment of BMI, which uses only weight and height as parameters for BMI calculation. A composite index such as continuous metabolic score may be theoretically more suitable for prediction of an increased AASI, as the resulting cMetS score represents an additive score of various cardiovascular risk factors. In this approach, the relative impact of obesity and BP would add up, which may have a better predictive value than each factor separately. Indeed, we were able to document that there was a significant correlation between cMetS and AASI as well as a better prediction of an increased AASI by using the cMetS. This is a novel finding, not previously reported in the literature.
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Figure 3. The ROC analysis of the impact of obesity and metabolic syndrome on arterial stiffness. AASI, ambulatory arterial stiffness; BMI, body mass index; cMetS, continuous metabolic syndrome; ROC, receiver operator curve.
The use of cMetS in children in relation to arterial stiffness was reported by Pandit et al26 and recently validated by Shaffie on more than 3000 schoolchildren aged 10–18 years.18 Pandit et al showed that the cMetS was more useful than individual components of the MS for assessment of atherosclerotic risk in children. Although both studies26 (our study) showed similar results that is better prediction of increased stiffness by a continuous cumulative risk score (cMetS), the studies are not directly comparable due to differences in methodology. Pandit et al measured arterial compliance and PWV during carotid intima media thickness measurement as a marker of arterial stiffness, whereas we used AASI from ABPM. A recent validation of the use of cMetS showed a cutoff of 2.9 for the presence or absence of MS. Our cMetS cutoff point for the presence or absence of an increased arterial stiffness (defined as AASI >0.3) is somewhat higher (4.5), but our definition of cMetS was slightly different. Our results are in agreement with Veijalainen et al,28 who also reported a significant association between cMetS and arterial stiffness in prepubertal children. The use of the cMetS as a marker of metabolic dysfunction in children was further supported in a recent study by Olza et al,29 who documented an association of cMetS with biomarkers of inflammation, endothelial damage, and cardiovascular disease. The advantage of adding various risk factors into one composite score such as the cMetS is further documented by the fact that a multivariate analysis did not show any significant predictor of an increased AASI except for height SDS. It may well be that one or more cardiovascular risk factors are variably present in various permutations in individual patients; therefore, a group analysis even with multivariate statistics may not identify a single risk factor which would be present in all patients. On the contrary, adding risk factors in cMetS would probably better reflect the reality in obese patients. The correlation between AASI and height SDS may be probably explained by the fact that the most obese patients were also the tallest (Table 1). There are several limitations of our study. It is a retrospective study on a convenient sample of pediatric patients. However, all study patients were recruited consecutively from all referred patients to our center and compared
with an age-matched control group of nonobese nonhypertensive children. Because of unavailability of waist circumference measurements in our children, we replaced this component with a BMI Z score in the calculation of the cMetS. For the BP component of the cMetS, we used the 24-hour MAP Z score rather than office BP. This is different from previous studies,18,26,30–35 in which authors used waist circumference and office BP. Although the main components (obesity, hypertension, glucose control, cholesterol, and HDL cholesterol) included in the cMetS remain similar across studies, their exact definition varies among published studies.36 The use of BMI scores instead of waist circumference can be potentially useful in pubertal children to avoid misclassification in body proportions.37 We also believe that the 24-hour BP monitoring, especially the 24-hour MAP reflecting both SBP and DBP provides a better assessment of the BP than the OBP.
Conclusion Our study shows that patients with higher degrees of obesity (BMI > 3.5) have a significantly higher arterial stiffness, which can be considered as a risk factor for future cardiovascular complications. A composite additive score such as the cMetS seems to be better predictor of an increased stiffness than individual risk factors. Acknowledgments K.P. collected the data, interpreted the results, drafted the manuscript, and acted as corresponding author. L.K. contributed to the conception and design of the study, interpretation of the results, and drafting of the article. E.V. designed the study, collected the data, and performed analysis on the samples. J.F. performed statistical analysis, interpreted the data, and drafted the manuscript.
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Survey in 2001. (Office of Public Health the Slovak Republic, 2004). Schwartz GJ, Mu~noz A, Schneider MF, Mak RH, Kaskel F, Warady BA, et al. New equations to estimate GFR in children with CKD. J Am Soc Nephrol 2009; 20:629–37. Zimmet P, Alberti KG, Kaufman F, Tajima N, Silink M, Arslanian S, et al. The metabolic syndrome in children and adolescents–an IDF consensus report. Pediatr Diabetes 2007;8:299–306. Hillier TA, Rousseau A, Lange C, Lepinay P, Cailleau M, Novak M, et al. Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia 2006;49:1528–35. Shafiee G, Kelishadi R, Heshmat R, Qorbani M, Motlagh ME, Aminaee T, et al. First report on the validity of a continuous metabolic syndrome score as an indicator for metabolic syndrome in a national sample of paediatric population—the CASPIAN-III study. Endokrynol Pol 2013;64:278–84. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;114: 555–76. W€uhl E, Witte K, Soergel M, Mehls O, Schaefer F; German Working Group on Pediatric Hypertension. Distribution of 24-h ambulatory blood pressure in children: normalized reference values and role of body dimensions. J Hypertens 2002;20:1995–2007. Aggoun Y, Farpour-Lambert NJ, Marchand LM, Golay E, Maggio AB, Beghetti M. Impaired endothelial and smooth muscle functions and arterial stiffness appear before puberty in obese children and are associated with elevated ambulatory blood pressure. Eur Heart J 2008;29:792–9. Iannuzzi A, Licenziati MR, Acampora C, Salvatore V, Auriemma L, Romano ML, et al. Increased carotid intima-media thickness and stiffness in obese children. Diabetes Care 2004;27:2506–8. Urbina EM, Kimball TR, McCoy CE, Khoury PR, Daniels SR, Dolan LM. Youth with obesity and obesity-related type 2 diabetes mellitus demonstrate abnormalities in carotid structure and function. Circulation 2009;119:2913–9. Urbina EM, Kimball TR, Khoury PR, Daniels SR, Dolan LM. Increased arterial stiffness is found in adolescents with obesity or obesity-related type 2 diabetes mellitus. J Hypertens 2010;28:1692–8. Urbina EM, Dolan LM, McCoy CE, Khoury PR, Daniels SR, Kimball TR. Relationship between elevated arterial stiffness and increased left ventricular
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mass in adolescents and young adults. J Pediatr 2011; 158:715–21. Pandit D, Chiplonkar S, Khadilkar A, Kinare A, Khadilkar V. Efficacy of a continuous metabolic syndrome score in Indian children for detecting subclinical atherosclerotic risk. Int J Obes 2011;35:1318–24. Sallay P, Degi A, Kerti A, Cseprekal O, Kis E, Szab o AJ, et al. Ambulatory arterial stiffness index in children after kidney transplantation. Pediatr Transplant 2013;17:598–604. Veijalainen A, Tompuri T, Laitinen T, Lintu N, Viitasalo A, Laaksonen DE, et al. Metabolic risk factors are associated with stiffness index, reflection index and finger skin temperature in children–Physical Activity and Nutrition in Children (PANIC) study. Circ J 2013;77:1281–8. Olza J, Aguilera CM, Gil-Campos M, Leis R, Bueno G, Valle M, et al. A continuous metabolic syndrome score is associated with specific biomarkers of inflammation and CVD risk in prepubertal children. Ann Nutr Metab 2015;66:72–9. Jolliffe CJ, Janssen I. Development of age-specific adolescent metabolic syndrome criteria that are linked to the Adult Treatment Panel III and International Diabetes Federation Criteria. J Am Coll Cardiol 2007;49:891–8. DuBose KD, Eisenmann JC, Donnelly JE. Aerobic fitness attenuates the metabolic syndrome score in
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normal-weight, at-risk-for-overweight, and overweight children. Pediatrics 2007;120:e1262–8. Eisenmann JC, Laurson KR, DuBose KD, Smith BK, Donnelly JE. Construct validity of a continuous metabolic syndrome score in children. Diabetol Metab Syndr 2010;2:8. Martınez-Vizcaıno V, Martınez MS, Aguilar FS, Martınez SS, Gutierrez RF, Lopez MS, et al. Validity of a single-factor model underlying the metabolic syndrome in children: a confirmatory factor analysis. Diabetes Care 2010;33:1370–2. Okosun IS, Boltri JM, Lyn R, Davis-Smith M. Continuous metabolic syndrome risk score, body mass index percentile, and leisure time physical activity in American Children. J Clin Hypertens 2010;12:636–44. Kelly AS, Steinberger J, Jacobs DR, Hong CP, Moran A, Sinaiko AR. Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood. Int J Pediatr Obes 2011;6:e283–9. Ford ES, Li C. Defining the metabolic syndrome in children and adolescents: will the real definition Please stand up? J Pediatr 2008;152:160–4.e13. Calcaterra V, Klersy C, Muratori T, Telli S, Caramagna C, Scaglia F, et al. Prevalence of metabolic syndrome (MS) in children and adolescents with varying degrees of obesity. Clin Endocrinol (Oxf) 2008;68: 868–72.
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Research Article
Is arterial stiffness predicted by continuous metabolic syndrome score in obese children? Katarina Prochotska, MDa,*, Laszlo Kovacs, MD, PhDa, Eva Vitariusova, MD, PhDa, and Janusz Feber, MD, FRCPCb a nd
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Department of Pediatrics, Comenius University Medical School, University Children’s Hospital, Bratislava, Slovakia; and b Division of Nephrology, Department of Pediatrics, Children’s Hospital of Eastern Ontario, Ottawa, Canada Manuscript received June 18, 2015 and accepted October 26, 2015
Abstract The aim of the article was to evaluate arterial stiffness, an early marker of increased cardiovascular risk, in relation to obesity. The continuous metabolic syndrome (cMetS) score was calculated as sum of Z score of mean arterial pressure, body mass index, serum glucose, triglyceride, and high-density lipoprotein cholesterol in 144 obese patients and 66 nonobese controls. Ambulatory arterial stiffness index (AASI) was calculated as 1 minus regression slope of diastolic on systolic blood pressure from ambulatory blood pressure measurements. The mean AASI increased progressively with severity of obesity. The receiver operator curve analysis of body mass index and AASI showed area under the curve of 0.64 0.06; cMetS area under the curve was 0.72 0.05 suggesting a better predictive power of the cMetS for an increased AASI (>0.3). Patients with obesity have significantly higher arterial stiffness. A composite score such as cMetS seems to be better predictor of an increased stiffness than individual risk factors. J Am Soc Hypertens 2015;-(-):1–8. Ó 2015 American Society of Hypertension. All rights reserved. Keywords: Arterial stiffness index; continuous metabolic syndrome; hypertension; obesity.
Introduction Atherosclerotic arterial lesions1 and arterial wall noncompliance2 can already be present in children and adolescents. The most relevant cardiovascular risk factors favoring atherogenic development are overweight and obesity.3 As changes in arterial function precede the development of atherosclerotic lesions,4 an increased arterial stiffness may be an early marker of increased cardiovascular risk,5 especially in obese patients with metabolic syndrome (MS). Across a wide age range, the diameter
Funding: This work was supported by Ministry of Health of the Slovak Republic under the project registration number 2012/5-UKBA-5, APVV-14-0234. Conflict of interest: Authors have no conflicts of interest to disclose. *Corresponding author: Katarina Prochotska, MD, 2nd Department of Pediatrics, Comenius University Medical School, University Children’s Hospital, Limbova 1, Bratislava 83340, Slovakia. Tel: (00421) 910-992-392; fax: (00421) 59-357-104. E-mail: [email protected]
and stiffness of arteries increase in proportion to the body mass index (BMI).6,7 The direct measurement of arterial stiffness by pulse wave velocity (PWV) is limited in children due to agerelated technical difficulties, fear from additional testing, and so forth. The arterial stiffness can be indirectly assessed by calculating the ambulatory arterial stiffness index (AASI)8 from ambulatory blood pressure monitoring (ABPM). AASI has been shown to strongly correlate with direct measures of arterial stiffness, such as PWV and augmentation index, and to provide prognostic information on cardiovascular mortality in adults.5,8 Studies have also shown that this index is associated with preclinical target organ damage in hypertensive adults.9,10 The literature data on AASI in children are scant and rather controversial. Simonetti et al found an increased AASI in hypertensive children regardless of their BMI,11 whereas Stergiou et al12 reported a strong correlation between AASI and BMI but no change of AASI with hypertension. Both these studies included children and adolescents with a relatively normal or slightly increased BMI without severe obesity. Recently, an increased AASI was
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also reported in obese children compared with nonobese controls.13 Children with severe obesity or MS are even more likely to have an increased arterial stiffness. The goal of our study was therefore to evaluate AASI in children with various degrees of obesity including severe obesity and MS in comparison with healthy controls. We also sought to analyze various risk factors for increased AASI, in particular the impact of ambulatory hypertension and MS.
Patients and Methods Patients We retrospectively analyzed all children and adolescents who were consecutively referred by community physicians to our center from May 2005 to September 2011 for assessment of obesity (BMI 2 standard deviation score [SDS]). None of patients experienced known chronic illness, and none received antihypertensive medications at the time of assessment. Patients with secondary forms of obesity were excluded. Control group recruited from children, who were referred for assessment of chronic illness such as growth retardation, chronic headache, chronic abdominal pain, and so forth. None of these children received any medication at the time of assessment, and none had any previous blood pressure (BP) problems. All enrolled patients had their weight, height, and BP measured, underwent ABPM, and had blood drawn for serum creatinine, serum uric acid, fasting cholesterol, triglycerides, and glucose levels; 24-hour urine collection was analyzed for albumin excretion. One hundred forty-four obese patients and 66 nonobese controls were included in the final analysis.
Anthropometry Weight and height were measured with a digital device (TONAVA TH200, Tonava a.s., Czech republic). BMI was calculated as body weight in kilograms divided by the square root of height in meter; absolute height and BMI values were subsequently transformed into SDS based on normative values obtained from Slovak children population.14
Laboratory Measurements Serum creatinine, uric acid, total cholesterol, highdensity lipoprotein (HDL) cholesterol, triglycerides, glucose levels, and urine albumin were measured by a Cobas Integra 800, Roche device. Non-HDL cholesterol was calculated as total cholesterol minus HDL cholesterol. The glomerular filtration rate was estimated using serum creatinine and height using the Schwartz formula.15 Obesity was defined as BMI equal or above 2.00 SDS.
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Metabolic Syndrome MS was defined in two different ways: Traditional metabolic syndrome definition according to the International Diabetes Federation criteria16 with two exceptions: (1) waist circumference was replaced with BMI Z score (BMI 2.0) and (2) office hypertension was replaced by ambulatory hypertension defined in the paragraph below. Continuous metabolic syndrome (cMetS) was calculated as a sum of the scores17,18: BMI Z score, mean arterial pressure (MAP) 24-hour Z score (from ABPM), Z scores for HDL cholesterol, triglycerides, and glucose levels were derived from the mean and standard deviation (SD) of our obese patients; the HDL cholesterol Z score was multiplied by 1 (inverted parameter for cardiovascular risk).
Classification of Obese Patients Obese patients were divided into three groups based on their BMI Z scores percentiles (33 percentile ¼ BMI Z score 3.5, 66% ¼ BMI Z score 4.5) obtained from the frequency distribution of BMI Z scores: BMI 1 ¼ patients with BMI Z scores 2.00 and <3.49. BMI 2 ¼ patients with BMI Z scores 3.50 and <4.49. BMI 3 ¼ patients with BMI Z scores >4.50.
Office Blood Pressure Office blood pressure (OBP) was measured only once on the day of the ABPM using a sphygmomanometer device (Tonometer 40, Chirana 400, Slovak Republic) on a nondominant arm with an appropriate cuff size. Obtained values in mm Hg were subsequently transformed into BP Z scores using the normative values.19
ABPM ABPM was measured using the validated oscillometric monitor Meditech-04 (SunTech Medical Instruments, Inc, USA). A cuff of an appropriate size was placed on a nondominant arm as per current guidelines. We defined the nighttime period as the time between midnight and 6 AM; the daytime period was defined as 8 AM to 8 PM. The obtained and verified ABPM results were subsequently imported into Chronos-Fit software (P. Zuther. S. Gorbey and B. Lemmer. Chronos-Fit 1.06. http://www.umm.uniheidelberg.de/inst/phar/lehre/chrono.html 2009). The following parameters were analyzed: average systolic (SBP) and diastolic blood pressure (DBP), MAP, their respective loads, and day-to-night differences. All these parameters were calculated for 24-hour, daytime, and nighttime periods separately. Absolute BP values were subsequently transformed into Z scores based on ABPM normative values.20 Blood pressure load (BPL) was defined as the number of BP values exceeding the 95th percentile of a given BP (SBP, DBP, and MAP) at a given period (24
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hour, daytime, and nighttime). The highest BPL obtained (either SBP or DPB or MAP) at any period was then defined as the maximum BPL and used for further analysis. For classification of ambulatory hypertension, we only used the ambulatory BP results regardless of the office BP. Our classification was as follows: Patients were considered to have ambulatory normotension if their 24-hour daytime and nighttime average SBP, DBP, and MAP were below the 95th percentile. If one or more of the BP parameters (either SBP or DBP or MAP) were above the 95th percentile at any period, patients were considered to have ambulatory hypertension regardless of their BP load. Patients were further classified as nondippers if their day-to-night BP (D/N BP) difference was below 10% (either SBP or DBP or MAP).
Ambulatory Arterial Stiffness Index From 24-hour ABPM recordings of each patient, the individual regression slopes of diastolic on SBPs were calculated.8 AASI was then defined as 1 minus the regression slope. Based on Simonetti et al, an AASI equal or higher than 0.3 was considered as elevated.11
Statistical Analysis Data are shown as mean SD if normally distributed or the median and interquartile range (25th and 75th percentile) in cases of abnormal distribution. Normal/abnormal distribution of variables was tested with the D’Agostino and Pearson omnibus normality test. Continuous variables in patient groups were compared using the analysis of variance with Tukey correction for multiple comparisons (normally distributed data) or the Kruskal–Wallis test with Dunn’s multiple comparison correction (not normally distributed data). Categorical variables (proportion of patients between groups) were compared using a Fisher test or a chi-square test. The difference in proportions was analyzed using the chi-square test for trend. Multiple regression analysis (Spearman correlation) was performed to analyze correlations between AASI and various clinical and BP parameters. Factor identified as statistically significant in the univariate analysis was subsequently included in the multivariate analysis. Linear regression was used to evaluate association between cMetS and AASI. Receiver operator curves were constructed to analyze whether BMI SDS and cMetS can predict an increased AASI defined as AASI higher than 0.3. Results were considered statistically significant if the P-value was below .05. Most statistical analyses were performed with the GraphPad Prism software, version 5.0 (GraphPad Software, La Jolla, CA, USA). The multivariate analysis was performed with Stata software (StataCorp LP, TX, USA). The study was approved by local ethics committee.
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3
Results Characteristics of Study Population The demographic data are shown in Table 1. The mean SD age of all patients was 14.7 2.9 years and did not differ between the groups. The study groups (obese patients with various degrees of obesity) and control group did not differ regarding gender distribution, kidney function, and urine albumin and serum glucose. Children in all obese groups had a higher BMI compared with the control group; obese groups differed in their BMI as per definition of groups. Obese patients with the highest degree of obesity (BMI 3) were significantly taller than controls. Obese patients in groups BMI 2 and BMI 3 had a higher serum uric acid and a lower HDL cholesterol than controls. Total cholesterol and triglyceride levels were higher in all three groups of obese patients; non-HDL cholesterol was significantly higher only in BMI 2 group. The percentage of patients with traditionally defined MS (traditional metabolic syndrome) was 39.6%, 54.2%, and 64.6% in BMI 1, BMI 2, and BMI 3, respectively (chisquare for trend, P ¼ .014). Similarly, the mean cMetS score increased progressively from 2.40 to 6.31 and from BMI 1 to BMI 3 groups (posttest for linear trend, r2 ¼ 0.27; P < .0001).
Blood Pressure OBP (systolic and diastolic) was significantly higher in all obese groups than in control group, but there was no difference in OBP between obese patients (Table 2). By definition, all ABPM parameters except for nighttime dipping were significantly higher in obese patients with ambulatory hypertension as compared with controls and obese patients without hypertension (Table 2). Overall, the ambulatory hypertension was found in 82 of 144 obese children (57%). Children with obesity had significantly lower D/N DBP and D/N MAP ratio, but D/N SBP ratio was not different among groups. The nondipping status was observed in 32 (52%) obese nonhypertensive patients and in 46 (56%) obese hypertensive children. Max BP load was significantly elevated in all obese patients compared with controls (Table 2).
Ambulatory Arterial Stiffness Index AASI results are shown in Table 2 and Figure 1. The mean SD AASI increased progressively from 0.35 0.17 in the control group to 0.40 0.16 in BMI 1, 0.44 0.14 in BMI 2, and 0.47 0.15 in BMI 3 (posttest for linear trend, r2 ¼ 0.084; P < .0001). From all parameters indicated in Tables 1 and 2, the AASI significantly correlated (multiple correlation) only with height SDS (r2 ¼ 0.22; P ¼ .009), serum cholesterol (r2 ¼ 0.23; P ¼ .007), D/N MAP (r2 ¼ 0.17; P ¼ .04),
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Table 1 Demographic and laboratory data Parameter
Control
Group BMI 1
Group BMI 2
Group BMI 3
P-Value
Number of patients Sex (M:F) Age (y) BMI (kg/m2) BMI (SDS) Height (cm) Height (SDS) Fasting glycemia Serum creatinine (mmol/L) Serum uric acid GFR (mL/min/1.73 m2) Urine albumin Serum cholesterol S HDL chol Serum triglycerides tMetS cMetS
66 35/31 15.09 (12.90, 16.47) 19.77 (17.31, 23.76) 0.06 (0.58, 1.19) 162.8 (150, 172.1) 0.66 (0.19, 1.22) 4.5 (4.2, 4.9) 62.28 13.84 274.5 (228.3, 313.3) 97.21 (84.91, 107.5) 6.92 (4.88, 57.83) 3.74 (3.3, 4.17) 1.38 (1.10, 1.72) 0.82 (0.69, 1.01)
48 26/22 14.12 (11.95, 16.17) 28.43*,y,z (26.92, 29.93) 3.04*,y,z (2.57, 3.23) 162 (154.3, 172.5) 0.93 (0.06, 1.54) 4.3 (3.8, 5.1) 61.39 14.61 318.5z (261.5, 368.5) 99.01 (81.63, 111.8) 6.85 (4.25, 12.15) 4.15* (3.75, 4.72) 1.13 (1.03, 1.25) 1.19* (0.85, 1.40) 39.58% 2.36 2.28y,z
48 31/17 14.7 (12.1, 17.05) 32.1*,z (31.13, 32.92) 4.00*,z (3.77, 4.21) 167 (155.3, 175.8) 1.33 (0.52, 1.82) 4.5 (3.9, 4.9) 61.00 12.13 349* (300.5, 402.5) 97.79 (86.53, 108.1) 7.62 (4.76, 13.81) 4.36* (3.86, 5.03) 0.97* (0.86, 1.17) 1.37* (1.07, 1.90) 54.17% 4.52 2.77z
48 31/17 14.74 (12.13, 16.59) 35.99* (33.62, 37.7) 5.24* (4.91, 5.88) 170* (163, 177.8) 1.52* (0.53, 2.65) 4.4 (4, 5) 63.78 11.61 419* (352, 480.5) 99.99 (84.75, 107.6) 7.62 (4.72, 21.25) 4.28* (3.77, 4.95) 1.00* (0.88, 1.2) 1.38* (0.98, 2.08) 64.58% 6.31 2.77
.13 .84 <.0001 <.0001 .019 .002 .006 .75 <.0001 .90 .52 <.0001 <.001 <.0001 .01 <.0001
BMI, body mass index; cMetS, continuous metabolic syndrome; GFR, glomerular filtration rate; SDS, standard deviation score; S HDL chol, serum high-density lipoprotein cholesterol; tMetS, traditional metabolic syndrome. Values are given as mean standard deviation (if normally distributed) or median and interquartile range (if not normally distributed). * Significantly different from control group (P < .05). y Significantly different from group BMI 2 (P < .05). z Significantly different from group BMI 3 (P < .05).
and the cMetS (r2 ¼ 0.21; P ¼ .01; Figure 2). Only borderline correlation was found between BMI SDS and AASI (r2 ¼ 0.16; P ¼ .059). However, on the multivariate analysis, none of these parameters correlated significantly with the AASI.
Further analysis of the impact of obesity on arterial stiffness using the receiver operator curve analysis of BMI and AASI showed area under the curve of 0.64 0.06; the cMetS area under the curve was 0.72 0.05 suggesting a better predictive power of the cMetS for an increased
Table 2 Office and 24-hour ambulatory blood pressure values (Z scores), blood pressure load (%), and ambulatory arterial stiffness index Parameter
Control
Group BMI 1
Group BMI 2
Group BMI 3
P-Value
Office SBP Office DBP MAP day MAP night MAP 24 hour SBP day SBP night SBP 24 hour DBP day DBP night DBP 24 hour BP load max D/N SBP D/N MAP D/N DBP AASI
0.26 (0.19, 0.75) 0.29 (0.04, 0.79) 0.61 (2.03, 0.61) 1.32 (2.30, 0.04) 1.25 (2.29, 0.41) 0.20 (1.29, 0.80) 0.45 (0.57, 1.12) 0.17 (0.94, 0.81) 0.38 (1.31, 0.34) 0.43 (0.68, 1.38) 0.35 (1.25, 0.55) 18.02 (7.96, 23.31) 1.12 (1.07, 1.17) 1.25 (1.16, 1.34) 1.23 (1.14, 1.30) 0.35 0.17
0.99* (0.42, 1.85) 1.34* (0.47, 1.55) 0.31 (1.02, 0.49) 0.47* (0.58, 1.10)* 0.01* (0.81, 0.79) 0.61 (0.21, 1.34) 1.04* (0.43, 2.07) 0.89* (0.04, 1.64) 0.16 (1.01, 1.25) 0.77* (0.11, 2.35) 0.37* (0.57, 1.78) 46.32* (23.66, 79.79) 1.11 (1.05, 1.16) 1.16* (1.09, 1.21) 1.18 (1.09, 1.25) 0.39 0.16
1.15* (0.55, 1.88) 0.72* (0.32, 1.46) 0.03 (0.81, 0.65) 0.80* (0.53, 1.66) 0.32* (0.51, 1.21) 0.42 (0.04, 1.29) 1.17* (0.54, 1.97) 0.78* (0.17, 1.75) 0.35 (1.24, 0.43) 0.54* (0.36, 1.14) 0.02 (0.87, 0.72) 55.05* (24.11, 68.13) 1.10 (1.06, 1.14) 1.13* (1.09, 1.18) 1.15 (1.11, 1.22) 0.44 0.14*
1.08* (0.64, 1.88) 1.28* (0.71, 1.75) 0.03 (0.94, 1.18) 1.20* (0.16, 2.17) 0.46* (0.46, 1.60) 0.86* (0.36, 1.83) 1.82* (1.00, 2.30) 1.26* (0.62, 2.35) 0.24 (1.45, 0.65) 0.90* (0.33, 1.86) 0.27 (0.89, 1.31) 60.00* (30.45, 83.33) 1.08 (1.04, 1.15) 1.12* (1.06, 1.21) 1.14* (1.06, 1.25) 0.47 0.15*
<.0001 <.0001 .09 <.0001 <.0001 <.001 <.0001 <.0001 .18 .004 .010 <.0001 .11 <.0001 .03 <.001
AASI, ambulatory arterial stiffness index; BMI, body mass index; BP load max, highest blood pressure load in SBP or DBP or MAP; DBP, diastolic blood pressure; D/N, day-to-night ratio; MAP, mean arterial pressure; SBP, systolic blood pressure. * Significantly different from control group (P < .05).
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Figure 1. AASI in relation to the severity of obesity. BMI 1 ¼ patients with BMI Z scores 2.00 and <3.49, BMI 2 ¼ patients with BMI Z scores 3.50 and <4.49, and BMI 3 ¼ patients with BMI Z scores >4.50. AASI, ambulatory arterial stiffness index; BMI, body mass index.
AASI (>0.3; Figure 3). The optimal predicting value of an increased AASI above 0.3 was the cMetS score of 4.567 with 55% sensitivity and 90% sensitivity (Figure 3).
Discussion The main findings of our study are (1) AASI is significantly elevated in children with higher degrees of obesity (BMI SDS > 3.5) compared with normal population and (2) a cMetS score correlated significantly with AASI and was a better predictor for an increased AASI than BMI SDS.
Figure 2. Correlation between AASI and cMetS. AASI, ambulatory arterial stiffness index; cMetS, continuous metabolic syndrome.
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5
An increased arterial stiffness in children has already been reported,21 as well as its relation to BMI3,7,22–24 and/or hypertension.25 In these studies, authors used a direct assessment of arterial stiffness by PWV assessment2,7,24,25 or a lumen diameter changes from the ultrasound tracing during intima media thickness measurement.3,21–23,26 We used ABPM to calculate the AASI, which is an indirect assessment of arterial stiffness and was shown to correlate with arterial stiffness measured by PWV.8 In addition, the AASI was found to be a good marker of cardiovascular risk in the population.5 There are only a limited number of studies using AASI for assessment of arterial stiffness in pediatric patients.11,12,27 Simonetti11 found an increased AASI in hypertensive children (mostly secondary hypertension) compared with normotensive children, but no significant correlation between AASI and BMI was detected. However, only 6% of hypertensive patients were obese and most patients (71%) experienced secondary hypertension.11 We also found only borderline correlation between AASI and BMI Z score, despite the fact that our children experienced significant obesity and more than 50% had MS. In contrast, Stergiou et al showed that AASI correlates with the BMI, but no difference was noted in AASI between hypertensive and normotensive children. However, in this study,12 only absolute BMI values in kg/m2 and BP values in mm Hg were used for the correlation analysis. This approach may potentially lead to skewed results in children of various age and height. We used ambulatory BP Z scores for the correlation analysis and did not find any significant correlation between AASI and any of the 24hour BP variables (expressed in Z scores) except for the nighttime dipping. In a recent study by Saner et al,13 the AASI was significantly higher in obese children compared with a matched lean control group. Interestingly, the AASI was influenced by BMI and pulse pressure, independently of BP values.13 As the BMI and BP may be interrelated, especially in obese patients, it is rather difficult to assess individual contributions of BP and BMI on the results of AASI. Saner et al13 suggest that there might be other factors involved in increased arterial stiffness such as sympathetic nervous system, leptin, oxidative stress, low vitamin D, and so forth. The traditional definition of obesity relies on the assessment of BMI, which uses only weight and height as parameters for BMI calculation. A composite index such as continuous metabolic score may be theoretically more suitable for prediction of an increased AASI, as the resulting cMetS score represents an additive score of various cardiovascular risk factors. In this approach, the relative impact of obesity and BP would add up, which may have a better predictive value than each factor separately. Indeed, we were able to document that there was a significant correlation between cMetS and AASI as well as a better prediction of an increased AASI by using the cMetS. This is a novel finding, not previously reported in the literature.
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Figure 3. The ROC analysis of the impact of obesity and metabolic syndrome on arterial stiffness. AASI, ambulatory arterial stiffness; BMI, body mass index; cMetS, continuous metabolic syndrome; ROC, receiver operator curve.
The use of cMetS in children in relation to arterial stiffness was reported by Pandit et al26 and recently validated by Shaffie on more than 3000 schoolchildren aged 10–18 years.18 Pandit et al showed that the cMetS was more useful than individual components of the MS for assessment of atherosclerotic risk in children. Although both studies26 (our study) showed similar results that is better prediction of increased stiffness by a continuous cumulative risk score (cMetS), the studies are not directly comparable due to differences in methodology. Pandit et al measured arterial compliance and PWV during carotid intima media thickness measurement as a marker of arterial stiffness, whereas we used AASI from ABPM. A recent validation of the use of cMetS showed a cutoff of 2.9 for the presence or absence of MS. Our cMetS cutoff point for the presence or absence of an increased arterial stiffness (defined as AASI >0.3) is somewhat higher (4.5), but our definition of cMetS was slightly different. Our results are in agreement with Veijalainen et al,28 who also reported a significant association between cMetS and arterial stiffness in prepubertal children. The use of the cMetS as a marker of metabolic dysfunction in children was further supported in a recent study by Olza et al,29 who documented an association of cMetS with biomarkers of inflammation, endothelial damage, and cardiovascular disease. The advantage of adding various risk factors into one composite score such as the cMetS is further documented by the fact that a multivariate analysis did not show any significant predictor of an increased AASI except for height SDS. It may well be that one or more cardiovascular risk factors are variably present in various permutations in individual patients; therefore, a group analysis even with multivariate statistics may not identify a single risk factor which would be present in all patients. On the contrary, adding risk factors in cMetS would probably better reflect the reality in obese patients. The correlation between AASI and height SDS may be probably explained by the fact that the most obese patients were also the tallest (Table 1). There are several limitations of our study. It is a retrospective study on a convenient sample of pediatric patients. However, all study patients were recruited consecutively from all referred patients to our center and compared
with an age-matched control group of nonobese nonhypertensive children. Because of unavailability of waist circumference measurements in our children, we replaced this component with a BMI Z score in the calculation of the cMetS. For the BP component of the cMetS, we used the 24-hour MAP Z score rather than office BP. This is different from previous studies,18,26,30–35 in which authors used waist circumference and office BP. Although the main components (obesity, hypertension, glucose control, cholesterol, and HDL cholesterol) included in the cMetS remain similar across studies, their exact definition varies among published studies.36 The use of BMI scores instead of waist circumference can be potentially useful in pubertal children to avoid misclassification in body proportions.37 We also believe that the 24-hour BP monitoring, especially the 24-hour MAP reflecting both SBP and DBP provides a better assessment of the BP than the OBP.
Conclusion Our study shows that patients with higher degrees of obesity (BMI > 3.5) have a significantly higher arterial stiffness, which can be considered as a risk factor for future cardiovascular complications. A composite additive score such as the cMetS seems to be better predictor of an increased stiffness than individual risk factors. Acknowledgments K.P. collected the data, interpreted the results, drafted the manuscript, and acted as corresponding author. L.K. contributed to the conception and design of the study, interpretation of the results, and drafting of the article. E.V. designed the study, collected the data, and performed analysis on the samples. J.F. performed statistical analysis, interpreted the data, and drafted the manuscript.
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