Clin Auton Res (2007) 17:165–171 DOI 10.1007/s10286-007-0411-6
Christopher L. Kaufman Daniel R. Kaiser Julia Steinberger Donald R. Dengel
Received: 27 August 2006 Accepted: 14 February 2007 Published online: 27 March 2007
C.L. Kaufman Æ D.R. Dengel School of Kinesiology University of Minnesota Minneapolis (MN), USA C.L. Kaufman (&) St. Paul Heart Clinic Dept. of Research 225 Smith Ave. N., Suite 400 St. Paul (MN) 55102, USA Tel.: +1-651/726-6885 Fax: +1-651/233-5091 E-Mail:
[email protected] D.R. Kaiser Dept. of Medicine University of Minnesota Minneapolis (MN), USA J. Steinberger Dept. of Pediatrics University of Minnesota Minneapolis (MN), USA
RESEARCH ARTICLE
Relationships between heart rate variability, vascular function, and adiposity in children
j Abstract Objective To examine
the relationships and interactions between cardiovascular autonomic nervous system (cANS) function, adiposity, and vascular function in children of varying levels of adiposity. Methods Participants were children (19 M, 17 F, age = 11.5 ± 0.1 years) who had cANS function assessed via heart rate variability (HRV) methods during resting conditions. Vascular function was assessed with brachial artery flow-mediated dilation (FMD) and nitroglycerininduced dilation. Spectral power of HRV was calculated for high frequency normalized units (HFnu; measure of PSNS activity) and low frequency:high frequency ratio (LF:HF; overall sympathovagal balance). A blood sample was drawn for measurement of fasting insulin, glucose, lipids, and Creactive protein (CRP). Results were reported as mean ± SEM. Results FMD peak dilation was positively related to HFnu (r = 0.48, P = 0.01) and negatively related to LF:HF (r = )0.51, P = 0.01) indicating that reduced
Introduction
j Key words cardiac autonomic function Æ flow-mediated dilation Æ pediatrics Æ cardiovascular disease
duced HRV and thus cardiovascular autonomic (cANS) dysfunction [16, 18, 20, 21, 29]. Power spectral analysis of HRV allows for the characterization of the relative balance between the parasympathetic nervous system (PSNS) and SNS and has indicated that in
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Several studies have indicated through the utilization of heart rate variability (HRV) methods that increased levels of adiposity in children are associated with re-
parasympathetic activity and states of dysfunctional sympathovagal balance were associated with decreased vascular function. After adjustment for confounding factors (insulin, CRP, age) and fat mass, the relationships between these measures of cANS and vascular function remained moderately strong and significant. Discussion These data indicate a relationship between cANS and vascular function that is independent of fat mass, inflammation (CRP), and fasting insulin in children of varying levels of adiposity. These relationships and the mechanisms by which they exist require further study to allow for the identification of appropriate therapies for children with high levels of adiposity given the likelihood of them having concomitant cANS and vascular dysfunction.
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children, increased levels of adiposity are characterized by decreased PSNS modulation of cardiac function [16, 18, 20, 21, 29] and either increased [16, 20, 21], decreased [18], or no difference [29] in SNS modulation. This is important because decreased heart rate variability and altered sympathovagal balance has been shown to be predictive of acute coronary syndromes and sudden cardiac death in certain populations [1, 14, 27]. Furthermore, cANS dysfunction via subsequent hemodynamic (i.e., hypertension) alterations could increase future cardiovascular disease (CVD) risk [17] since increased sympathetic nervous system (SNS) activity in children has been shown to be associated with an increased prevalence of hypertension in adulthood and thus may relate the role of the SNS in altering vascular function and tone [29]. Brachial artery flow-mediated dilation (FMD) assesses endothelial function by examining the vasodilation response of vascular smooth muscle from the release of nitric oxide from endothelial cells after hyperemia [5] and has been shown to be a sensitive measure of early CVD [15, 19]. Recent pediatric research has shown that development of obesity in childhood is associated with endothelial cell dysfunction as assessed by FMD [13, 26, 28]. Several metabolic alterations (i.e., hyperinsulinemia, dyslipidemia, increased adipokines) have been implicated in the pathology of this dysfunction [13, 30]. It has been suggested that altered autonomic function may contribute to this blunted vasodilatory response [8]. Recent studies indicate that acute increases in SNS activity decrease FMD of the brachial artery [6, 10]. Furthermore, a recent study by Iellamo et al. (2006) suggested that increased peripheral SNS activity and reduced PSNS cardiac modulation was associated with impairment in endothelial function in healthy firstdegree relatives of diabetic patients [11]. However, the potential confounding effects of level of adiposity, blood insulin level, and inflammation were not addressed in this study. Interestingly, many of the risk factors that reduce FMD (i.e., hyperinsulinemia, increased inflammation, hyperleptinemia) also alter cANS function towards a state of sympathetic dominance. However, there is no previous literature examining the relations and interactions between cANS function, adiposity, and vascular function in pediatric populations. Since previous literature has identified that both cANS and vascular dysfunction are strongly linked to increased levels of adiposity and other risk factors of CVD, studying the relationships between these two physiologic systems may provide useful insight into the CVD risk continuum in children. The purpose of this study was to examine the relationships and interactions between adiposity and
cANS and vascular function in children with varying levels of adiposity. We hypothesized that cANS function and vascular function would be significantly related and the level of adiposity would be associated with cANS and vascular function.
Method j Participant population A total of 36 children (19 male, 17 female, age = 11.5 ± 0.1 years, Tanner score = 1.9 ± 0.1) were recruited from the greater Minneapolis metro area. All studies took place at the University of Minnesota General Clinical Research Center. Participants were healthy children between the ages of 10 and 13 years. ‘‘Healthy’’ was defined as being free of any major cardiovascular or metabolic disease diagnoses such as hypertension or diabetes. Children that were affected by disease states that effect cardiovascular function and/or cardiac electrophysiology, which included orthostatic intolerance, unexplained syncopal episodes and obstructive sleep apnea, were not allowed to participate in the study. It was determined whether a child met any of the above-mentioned exclusion criteria by a medical history questionnaire after the informed consent process and by performing a resting electrocardiogram prior to testing. Due to the use of detailed fliers for recruitment, all children that were initially screened were eligible for and did participate in study. j Experimental protocol All components of a standard informed consent including purpose, risks, and benefits were fully explained to each child and their parent(s). Written informed consent from the parents/guardians and assent from the participants were then obtained. All methods used in the study were reviewed and approved by the University of Minnesota Institutional Review Board. All participants were studied after a 10-h overnight fast. Medical history was ascertained via questionnaire. A blood draw was performed for blood sample analyses. Height and weight was measured using a wall-mounted stadiometer and digital weight scale (Model 5002, Scale-Tronix Inc., Wheaton, IL). Body mass index (BMI; kg/m2) was calculated as the body weight (kg) divided by height squared (m2). Assessment of pubertal development (Tanner score) was determined through a brief physical examination by a trained pediatrician. A dual X-ray absorptiometry (DXA) scan was then performed for the determination of total body fat % and trunk fat % (Prodigy, 3M, Madison, WI; software version 6.7). Participants were then prepped for electrode placement for measurement of heart rate via a 3-lead electrocardiogram (ECG). The ECG (Lead II) and blood pressure (BP) was continuously recorded using an automated blood pressure cuff and automated tonometer (Colin Pilot 7000, Colin Medical Instruments Corp., San Antonio, TX). Resting blood pressure was obtained after the participant had been sitting quietly for 5 minute. Three measurements were taken and the average of the three were reported for systolic and diastolic blood pressure. Due to findings in pilot studies that indicated children find it difficult to pace their breathing to a set cadence, no constraints were imposed on breathing frequency. Participants laid flat on their back on a cushioned bed for approximately 15 minutes to ensure a resting state was attained. Following the initial rest period, the participant continued to lie relaxed for an additional 15 minutes to record resting ECG and BP measures. Vascular function was then assessed using standard flow-mediated dilation and nitroglycerin administration methods [5].
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j HRV analyses The ECG and BP waveforms were digitally recorded continuously using a desktop computer and WinDaq Pro data collection software (DATAQ Instruments Inc., Akron, OH). Each signal was sampled at 500 Hz throughout all testing. The WinDaq Pro software allowed for instantaneous analog to digital conversion of the ECG and BP signal with recordings stored for latter off-line analysis. Files were imported into a software program (Matlab, The MathWorks, Inc., Natick, MA) for computation of standard time- (standard deviation of normal R–R intervals, SDRR; square root of the mean squared differences of successive R-R intervals, RMSSD) and frequencydomain HRV variables based on current recommendations [24]. The last 5 minute of the 15 minute resting period was utilized for calculation of all resting HRV variables. Each 5 minute segment was manually reviewed for ectopic beats or arrhythmias and segments containing such alterations of normal electrophysiological function were excluded from analysis. Power spectral density of the R–R time series was calculated using nonparametric methods (fast-Fourier transform) after being passed through a Hamming window. Three frequency bands [verylow frequency (0–0.03 Hz), low frequency (LF, 0.04–0.15 Hz; when expressed in normalized units is indicative of primarily SNS modulation with some influence from PSNS modulation), high frequency (HF, 0.15–0.4 Hz; indicative of purely PSNS modulation of cardiac function) were obtained from the short-term HRV recordings, but only LF and HF were considered in our analyses because the VLF component is considered a dubious measure due to its lack of physiologic meaning and interpretation. Therefore, total power within the power spectrum (0.04–0.40 Hz) and the normalized units of the LF and HF components (normalized unit = LF or HF/total power)VLF) were reported. In addition, the LF:HF ratio was calculated as a measure of relative sympathovagal balance [24]. Normalized units and LF:HF allow for an accurate assessment of the balanced modulations of each branch of the autonomic nervous system while not being influenced by the total power within the power spectrum [20]. Reproducibility data from our lab have shown a mean difference of 0.01 ± 0.02 s for SDRR (coefficient of variation = 20.6%), 1.64 ± 4.0 s for RMSSD (coefficient of variation = 4.1%), 0.05 ± 0.1 for the LF:HF ratio (coefficient of variation = 5.7%), 1.28 ± 2.4 for LFnu (coefficient of variation = 3.1%), and 1.27 ± 2.4 for HFnu (coefficient of variation = 4.6%) when testing 5 healthy young adults one week apart (unpublished data). j Vascular function assessment Endothelium-dependent dilation was assessed with flow-mediated dilation (FMD) methods as previously reported [13]. Briefly, a standard ultrasound machine (Image Point Hx, Philips Medical, Bothell, WA) with a 7.5-MHz linear array transducer was used to obtain images of the left brachial artery (approximately 2–10 cm proximal to the elbow). Resting arterial diameter was measured and was followed by 5 minute of cuff occlusion (cuff inflation to 200 mmHg) distal to the elbow. Following cuff occlusion, the brachial artery image was recorded for 3 minute for the calculation of peak percent dilation (FMD peak) using resting arterial diameter as the reference baseline. Following the FMD procedure, 0.3 mg of nitroglycerin was administered sublingually and the same brachial artery segment was imaged for 5 minute for determination of endotheliumindependent (EID) vasodilation. Images were captured at the R wave of the electrocardiogram (end-diastolic diameter) and stored for later off-line analysis. Electronic wall-tracking software (CVI, Information Integrity, Boston, MA) was used for determining arterial diameter at rest and during the FMD and nitroglycerin studies. Reproducibility data from our lab has shown a mean difference of 0.53% ± 0.28% (coefficient of variation = 11.1%) for analyses separated by 1 week in 10 young, healthy persons [12].
Table 1 Clinical and laboratory data for 36 children Variable
Mean ± SEM
Range
Age (year) Tanner Score Weight (kg) BMI (kg/m2) Total Body Fat (%) Trunk Body Fat (%) Glucose (mg/dl) Insulin (mU/l) Total Cholesterol (mg/dl) HDL (mg/dl) LDL (mg/dl) Triglycerides (mg/dl) CRP (mg/l) SBP (mmHg) DBP (mmHg)
11.5 1.9 62.1 26.3 38.9 39.9 89.8 13.9 160.9 41.0 98.6 107.9 3.5 112.4 63.2
10–13 1–3 26.1–113.6 14.7–42.2 11.8–56.7 9.6–58.7 73–126 3.0–43.0 116.0–199.0 25.0–70.0 66.0–140.0 41.0–433.0 0.2–18.8 96–136 41–79
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.1 0.1 3.2 1.2 2.0 2.3 1.6 1.6 3.9 1.5 3.3 12.7 1.0 1.5 1.8
Note: Data are presented as mean ± standard error of the mean; BMI = body mass index; CRP = C-reactive protein; SBP = systolic blood pressure; DBP = diastolic blood pressure j Blood collection/analysis After an overnight fast, blood was drawn by vena puncture for analysis of fasting blood glucose, insulin, lipids, and C-reactive protein (CRP). Whole blood samples were kept on ice and centrifuged within 20 minute for subsequent analysis. Blood glucose, cholesterol, and triglycerides were determined by colorimetic reflectance spectrophotometry. Insulin was determined by chemiluminescent immunoassay. CRP was measured with an ultra-sensitive assay utilizing rate nephlometry. j Data analysis Data were analyzed using Pearson’s r to examine relationships between HRV variables and measures of vascular function. For all measurements, means and standard error of the mean were calculated and reported. Measurements of cANS function (SDRR, RMSSD, LF, HF, and LF:HF), vascular function (FMD peak dilation and EID), and body fatness (total body fat %) were examined separately as outcome variables. CRP was log-transformed (i.e., Y = Log X) and LFnu, HFnu, and LF:HF were transformed with the natural logarithm (i.e., Y = LnX) for data analyses due to their skewed distribution. Partial correlation analysis was performed to evaluate the interaction between 2 of the 3 parameters (body fatness, HRV parameters, vascular function parameters) using the other factor and confounding factors (age, log-CRP, Tanner stage, and insulin) as covariates. All statistical procedures were performed using SPSS 13.0 (SPSS Inc., Chicago, IL). An alpha level of 0.05 was used to denote statistical significance.
Results Demographics of the pediatric sample are provided in Table 1. Blood sampling was not obtained due to technical problems with venous access for two participants. All HRV measures of cANS function and measures of vascular function are presented in Table 2.
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Table 2 HRV and vascular function parameters for 36 children Variable
Mean ± SEM
Range
RR int. (s) SDRR (s) RMSSD (ms) LFnu (%) Ln-LFnu (%) HFnu (%) Ln-HFnu ( %) LF:HF Ln-LF:HF Total Power (ms2) FMD peak (%) EID (%)
0.847 0.092 110.8 51.1 3.90 48.9 3.85 1.21 0.05 1644.1 5.14 22.8
0.69–1.15 0.03–0.15 22.4–110.8 21.8–78.9 3.08–4.37 21.1–78.3 3.05–4.36 0.28–3.73 )1.27–1.32 410.3–3899.9 1.1–8.5 9.0–42.4
± ± ± ± ± ± ± ± ± ± ± ±
0.02 0.01 3.9 2.1 0.05 2.1 0.05 0.12 0.09 148.6 0.3 1.2
Note: SDRR = standard deviation of RR interval, RMSSD = root mean sum of squares differences of RR interval, LF nu = low frequency normalized units, LnLFnu = natural logarithm of low frequency normalized units, HF nu = high frequency normalized units, Ln-HFnu = natural logarithm of high frequency normalized units, LF:HF = low frequency to high frequency ratio, LnLF:HF = natural logarithm of low frequency to high frequency ratio, FMD peak = peak dilation after flow-mediated dilation technique, EID = endothelium-independent dilation Table 3 Correlation matrix among Total Body Fat %, FMD peak, and HRV variables Total Body Fat %
SDRR (s) RMSSD (ms) Ln-LFnu (%) Ln-HFnu (%) Ln-LF:HF FMD peak (%)
FMD peak (%)
r
ra
r
ra
)0.34* )0.30 0.44** )0.43* 0.44** )0.34*
)0.28 )0.10 0.18 )0.16 0.18 )0.05
0.41* 0.31 )0.52** 0.48** )0.51** 1.00
0.35* 0.22 )0.48** 0.47** )0.48** 1.00
Note: FMD peak = peak dilation after flow-mediated dilation technique, SDRR = standard deviation of RR interval, RMSSD = root mean sum of squares differences of RR interval, Ln-LFnu = natural logarithm of low frequency normalized units, Ln-HFnu = natural logarithm of high frequency normalized units, Ln-LF:HF = natural logarithm of low frequency to high frequency ratio; r = Pearson’s product-moment correlation coefficient non-adjusted, ra = Pearson’s product-moment correlation coefficient adjusted for age, Tanner stage, insulin, and log-CRP; *P < .05, **P < .01
j Relationships and interactions between cANS and vascular function and body fatness Simple correlation analysis in all participants revealed significant relationships between cANS measures, FMD peak dilation, and total body fat % (Table 3). The relationships of FMD peak dilation with LF:HF and HFnu are presented in Figure 1 (Panel A & B). After adjustment for confounding factors (age, Tanner stage, insulin, and log-CRP), the relationships between FMD peak dilation and HRV variables (SDRR, LFnu, HFnu, and LF:HF) remained significant. The associations between total body fat % and FMD peak dilation and HRV measures were
Fig. 1 Relationship between FMD peak dilation with LF:HF (Panel A) and HFnu (Panel B)
not significant after adjustment for confounding factors. No significant relationships were found between measures of cANS function and EID (data not shown). Partial correlation analyses of 2 of the 3 factors (total body fat %, FMD peak, HRV variables) while using the other factor and confounding factors as covariates indicated a negative correlation (r = )0.51, P = 0.01) between Ln-LF:HF and FMD peak and a positive correlation (r = 0.50, P = 0.01) between Ln-HFnu and FMD peak after adjustment for total body fat %. If trunk body fat % was used as the measure of adiposity, the relationships between Ln-LF:HF and FMD peak and Ln-HFnu and FMD peak remained significant (i.e., Ln-LF:HF and FMD peak, r = )0.42, P = 0.01; Ln-HFnu and FMD peak, r = 0.39, P = 0.02). Total body fat % was not significantly related to FMD peak (r = 0.25, P = 0.22) when the HRV variables, age, Tanner stage, insulin, and log-CRP were used as covariates. In addition, total body fat % was not significantly related to LnLF:HF (r = 0.23, P = 0.22), Ln-HFnu (r = )0.21, P = 0.28), and Ln-LFnu (r = 0.23, P = 0.22) when confounding variables and FMD peak were the covariates.
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Discussion The aim of the present study was to identify relationships and potential interactions between measures of cANS and vascular function and level of adiposity in children. Pediatric populations present an ideal ‘‘model’’ to study these effects since the incidence of potentially confounding variables such as hypertension, diabetes, tobacco use, and hyperlipidemia are minimal. The main finding of the present study was that cANS and vascular function are significantly related and that this relationship is independent of level of adiposity, blood insulin level, inflammation (CRP), Tanner stage, and age. Furthermore, our data indicate that increased levels of adiposity are associated with cANS dysfunction and a reduced capacity of endothelium-dependent vasodilation. Interestingly, we found that the relationship of adiposity with cANS and vascular function was not significant after adjusting for confounders which suggests that other factors (i.e., insulin, inflammation) associated with increased levels of adiposity may be more influential in these relationships. The cANS dysfunction observed with increasing levels of adiposity was characterized by an increased sympathovagal balance (suggesting an increased SNS modulation) and decreased HFnu, which indicates reduced PSNS modulation on cardiac function. These data are in agreement with previous literature that has shown that increased body fatness is associated with PSNS withdrawal (reduced HFnu) [16, 18, 20, 21, 29]. These data further support previous research that has showed that increased levels of adiposity are associated with decreased FMD peak dilation [28]. Consequently, our data reiterate the negative effect of increased body fatness on endothelial function. It has been hypothesized that increased adiposity influences endothelial function through elevations in plasma insulin and CRP [30], reactive oxygen species [23] and various cytokines [4]. The results from our study indicate significant relationships between cANS and vascular function and suggest a potential interaction between the two physiologic functions. The mechanisms by which increased levels of adiposity alters cANS function are unknown, but those hypothesized to be potentially responsible are hyperinsulinemia [7], free fatty acids [9], and inflammation [25]. Interestingly, we found that after adjusting for insulin, CRP, and age that the relationship between adiposity and cANS function was not significant, which supports, but does not prove, this hypothesis. As noted prior, the mechanisms associated with increased adiposity and cANS dysfunction may also be involved in altering vascular function. Therefore, it is physiologically plausible to hypothesize that vascular function may be compro-
mised in situations in which there is increased SNS modulation (and decreased PSNS modulation) or vice versa. Indeed, it has been shown that nitric oxide (NO) may be involved in down-regulation of sympathetic outflow and thus decreased NO synthesis might contribute to overactivity of the SNS [22]. Our finding of a significant negative correlation between LF:HF and FMD peak dilation supports this concept. However, the exact mechanism by which these physiologies interact cannot be obtained from the results of this study. This study was not designed to show that altered cANS function due to increasing levels of adiposity causes endothelial dysfunction or vice versa, but rather was a preliminary study that was aimed at determining if there were any potential relationships or interactions between the two physiologic systems. Indeed, no cause-effect relationship can be determined from this study since only associations among different variables were examined. Further research is needed to elucidate these mechanisms. The primary limitation of the work presented is the small sample size. Because of this aspect of the study, broad generalizations of the results to larger populations should be cautioned. No data were collected on the participants’ daily physical activity level. However, differences in level of physical activity could contribute to differences in cANS and vascular function and thus should be considered a limitation of the study. Our interpretation of total cANS function may be limited by the fact that our measure of cANS function was quantified solely by resting HRV. Additional non-invasive tests such as cold-pressor test, Valsalva maneuver, deep breathing, and/or tilttable testing may have provided a more in-depth interpretation. The effects of respiratory rate on heart rate are well known. All efforts were made to ensure that the children were completely comfortable and relaxed to allow for breathing at normal physiologic respiratory rate (typically 14–16 breaths per minute or 0.25 hertz) [2]. It was presumed that the respiratory frequency component of HRV occurred at approximately 0.25 Hz, which is located in the HF frequency range. Therefore, the interpretation of the LF component of the power spectral analysis of HRV should not be limited. Furthermore, recent evidence indicates that controlling respiratory rate (e.g., with a metronome) does not significantly affect the HF component of power spectral analyses [3]. The methods employed in the study appear justified, yet we cannot exclude the possibility that not controlling respiratory rate had some effect on our measures of cANS function and thus must consider this a limitation of the study. In conclusion, the implications of cANS and vascular dysfunction for the risk of future CVD in children are disconcerting. It is well known that the
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physiologic processes that initiate CVD begins in childhood irrespective of the presence of obesity. It appears that abnormal increases in adiposity early in life may potentiate these processes. Our findings indicate that increased levels of adiposity are associated with negative alterations in cANS and vascular function and that interactions exist between these physiologic systems. We contend that these measures might be conceivable early signs to predict cardiovascular health as an adolescent and into adulthood. However, further studies are needed to elucidate the mechanisms of these alterations and interactions to
provide a basis by which non-pharmacologic and/or pharmacologic therapies can be tested and administered. j Acknowledgements We wish to acknowledge the contributions of Mr. Eric Williamson, Mr. Joseph Warpeha and Mr. Brett Bruininks in data collection for this project. Furthermore, we would like to thank Dr. Aaron Kelly for reviewing this manuscript and providing important insight. Funding Support: This work was supported in part by Minnesota Obesity Center Grant #: 1 P30 DK 50456–08 (d.r.k.), and GCRC: M01-RR00400, General Clinical Research Center Program, NCRR/NIH.
References 1. Astrup AS, Tarnow L, Rossing P, Hansen BV, Hilsted J, Parving HH (2006) Cardiac autonomic neuropathy predicts cardiovascular morbidity and mortality in type 1 diabetic patients with diabetic nephropathy. Diabetes Care 29:334–339 2. Bar-Or O, Rowland TW (eds) (2004) Pediatric exercise medicine. Human Kinetics: Champagne, IL 3. Bloomfield DM, Magnano A, Bigger T Jr., Rivadeneira H, Parides M, Steinman RC (2001) Comparison of spontaneous vs. metronome-guided breathing on assessment of vagal modulation using RR variability. Am J Physiol Heart Circ Physiol 280: H1145– H1150 4. Cabellero AE (2003) Endothelial dysfunction in obesity and insulin resistance: a road to diabetes and heart disease. Obes Res 11:1278–1289 5. Celemajer DS, Sorenson KE, Gooch VM, et al. (1992) Non-invasive detection of endothelial dysfunction in children and adults at risk of atherosclerosis. Lancet 340:1111–1115 6. Dyson KS, Shoemaker JK, Hughson RL (2006) Effect of acute sympathetic nervous system activation on flowmediated dilation of brachial artery. Am J Physiol Heart Circ Physiol 290:H1446–H1453 7. Emdin M, Gastaldelli A, Muscelli E, et al. (2001) Hyperinsulinemia and autonomic nervous system dysfunction in obesity: effects of weight loss. Circulation 103:513–519 8. Engelke KA, Williams MM, Dietz NM, Joyner MJ (1997) Does sympathetic activation blunt nitric oxide-mediated hyperemia in the human forearm? Clin Auton Res 7:85–91 9. Grekin RJ, Dumont CJ, Vollmer AP, Watts SW, Webb RC (1997) Mechanisms in the pressor effect of hepatic portal venous fatty acid infusion. Am J Physiol 273:R324–R330
10. Hijmering ML, Stroes ESG, Olijhoek J, Hutten BA, Blankestijn PJ, Rabelink TJ (2002) Sympathetic activation markedly reduces endothelium-dependent, flow-mediated vasodilation. J Am Coll Cardiol 39:683–688 11. Iellamo F, Tesauro M, Rizza S, et al. (2006) Concomitant impairment of endothelial function and neural cardiovascular regulation in offspring of type 2 diabetic subjects. Hypertension 48:418–423 12. Kelly AS, Kaiser DR, Dengel DR, Bank AJ (2004) Comparison of B-mode and echo tracking methods of assessing flow-mediated dilation. Ultrasound Med Biol 30:1447–1449. 13. Kelly AS, Wetzsteon RJ, Kaiser DR, Steinberger J, Bank AJ, Dengel DR (2004) Inflammation, insulin, and endothelial function in overweight children and adolescents: the role of exercise. J Pediatr 145:731–736 14. Kleiger RE, Miller JP, Bigger JT, Moss AJ (1987) Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 59:256–262 15. Kobayashi K, Akishita M, Yu W, Hashimoto M, Ohni M, Toba K (2004) Interrelationship between non-invasive measurements of atherosclerosis: flowmediated dilation of brachial artery, carotid intima-media thickness and pulse wave velocity. Atherosclerosis 173:13–18 16. Martini G, Riva P, Rabbia F, et al. (2001) Heart rate variability in childhood obesity. Clin Auton Res 11:87–91 17. Montani JP, Antic V, Yang Z, Dulloo A (2002) Pathways from obesity to hypertension: from the perspective of a vicious triangle. Int J Obes 26:S28–S38
18. Nagai N, Matsumoto T, Kita H, Moritani T (2003) Autonomic nervous system activity and the state and development of obesity in Japanese school children. Obes Res 11:25–32 19. Neunteufl T, Katzenschlager R, Hassan A, et al. (1997) Systemic endothelial dysfunction is related to the extent and severity of coronary artery disease. Atherosclerosis 129:111–118 20. Rabbia F, Silke B, Conterno A, et al. (2003) Assessment of cardiac autonomic modulation during adolescent obesity. Obes Res 11:541–548 21. Riva G, Martini G, Milan A, Paglieri C, Chiandussi L, Veglio F (2001) Obesity and autonomic function in adolescence. Clin Exp Hypertens 23:57–67 22. Scherrer U, Sartori C (2000) Defective nitric oxide synthesis: a link between metabolic insulin resistance, sympathetic overactivity, and cardiovascular morbidity. Eur J Endocrinol 142:315– 323 23. Taniyama Y, Griendling KK (2003) Reactive oxygen species in the vasculature: molecular and cellular mechanisms. Hypertension 42:1075–1081 24. Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043–1065 25. Toni R, Malaguti A, Castorina S, Roti E, Lechan RM (2004) New paradigms in neuroendocrinology: relationships between obesity, systemic inflammation and the neuroendocrine system. J Endocrinol Invest 27:182–186 26. Tounian P, Aggoun Y, Dubern B, et al. (2001) Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet 358:1400–1404
171
27. Tsuji H, Venditti FJ, Manders ES, et al. (1994) Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study. Circulation 91:878–883 28. Woo KS, Chook P, Yu CW, et al. (2004) Overweight in children is associated with arterial endothelial dysfunction and intima-media thickening. Int J Obes 28:852–857
29. Yakinci C, Mungen B, Karabiber H, Tayfun M, Evereklioglu C (2000) Autonomic nervous system functions in obese children. Brain Dev 22:151– 153
30. Yudkin JS, Stehouwer CDA, Emeis JJ, Coppack SW (1999) C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue? Arterioscler Thomb Vasc Biol 19:972–978
