Heart Rate Variability and Cardiovascular Risk Factors in Adolescent Boys

Breno Q. Farah, MS1, Mauro V. G. Barros, PhD1, Babu Balagopal, PhD2, and Raphael M. Ritti-Dias, PhD1

Objective To establish reference values of heart rate variability (HRV)measures in a cohort of adolescent boys andto determine the relationship between HRV and the clustering of risk factors (RFs) for cardiovascular disease.Study design This cross-sectional study included 1152 adolescent boys (age: 16.6� 1.2 years old). Demographicdata, health-related habits, obesity indicators, and blood pressure were evaluated. HRVmeasures of time (SD of allRR intervals, rootmeansquareof the squareddifferencesbetweenadjacent normalRR intervals, and thepercentageof adjacent intervals over 50 ms) and frequency domains were assessed (low [LF] and high [HF] frequency).Results The components ofHRVwereRR interval (827� 128ms), SDof all RR intervals (61.9�23.5ms), rootmeansquare of the squared differences between adjacent normal RR intervals (54.5 � 29.4 ms), percentage of adjacentintervals over 50ms (29.4� 20.4%), LF (53� 16 nu), HF (47� 16), and LF/HF (1.44� 1.08). Greater sympathetic andlower parasympatheticmodulation at restwere associatedwith higher adiposity, higher bloodpressure andphysicalinactivity. Adolescents with 2 or more RFs also presented lower HRV than subjects with no RFs (P < .001).Conclusions The study has provided descriptive indicators that help the interpretation of HRV results in adoles-cents. Lower HRV measures are associated with the clustering of cardiovascular RFs. (J Pediatr 2014;-:---).

The autonomic nervous system plays a major role in the regulation of the cardiovascular system.1 Cardiac autonomicmodulation can be assessed based on heart rate variability (HRV),2 defined as the variability between consecutive beats.HRV is considered a potent marker of cardiovascular risk in different groups,2-7 providing information about early

changes in cardiac autonomic control.1,2 HRV is commonly assessed by analyzing the heart beat intervals using time and fre-quency domain techniques.2 Although time domain measures either the heart rate at any point in time or the intervals betweensuccessive normal complexes, the frequency domain analysis describes the periodic oscillations of the heart rate signal decom-posed at different frequencies and amplitudes.2

A low HRV is indicative of reduced parasympathetic cardiac control and has been associated with various conditions such ascardiovascular disease, diabetes, sleep disorders, and emotional issues.2,8 Although the relationship between lowHRVand compro-mised health has been extensively studied in adults, such studies are scarce in adolescents. The reference values of HRV in children(5-10 years old)9 and adults (mean age 41 � 9 years old)10 have been established, but surprisingly, they are not available in ado-lescents. Given the broad array of health issues associated with lowered HRV and the challenges involved in the interpretationof such issues in the youth, it is important to fill this gap and establish reference standards for HRV in adolescents.

Previous studies have shown that being overweight,11,12 abdominal obesity,13 and physical inactivity13,14 affect HRV mea-sures in adolescents. However, the impact of blood pressure on HRV remains unclear. In a subsample of the Bogalusa HeartStudy, Urbina et al15 found a trend toward higher sympathetic and lower parasympathetic measures occurring in adolescentmales with higher diastolic blood pressure. Prior studies11-16 that analyzed the association between cardiovascular risk factors(RFs) and HRV in adolescents typically had small samples and considered the RFs separately, and it is known that clustering ofRFs is an important determinant of cardiovascular risk.17-19 The potential impact of clustering of cardiovascular RFs onHRV inadolescents has not been previously reported.

The aims of the current study were to establish reference values of HRV measures in a large cohort of adolescent boys and todetermine the relationship between HRV measures and the clustering of RFs for cardiovascular disease.

HF High frequency

HRV Heart rate variability

LF Low frequency

PNN50 Percentage of adjacent inte

RFs Risk factors

RMSSD Root mean square of the s

SDNN SD of all RR intervals

Methods

The cross-sectional study protocol was approved by the ethics committee of the University of Pernambuco in compliance withthe Brazilian National Research Ethics System Guidelines. The target population was limited to high school students between

14 and 19 years old. The participants were sampled from the students of the

From the 1Associate Graduate Program in PhysicalEducation, University of Pernambuco, Recife, PE, Brazil;and 2Nemours Children’s Clinic and Mayo Clinic Collegeof Medicine, Jacksonville, FL

Supported by the Conselho Nacional de Desenvolvi-mento Cient�ıfico e Tecnol�ogico (grant 481067/2010-8)and Coordenac~ao de Aperfeicoamento de Pessoal deN�ıvel Superior. The authors declare no conflicts of inter-est.

0022-3476/$ - see front matter. Copyright ª 2014 Elsevier Inc.

All rights reserved.

http://dx.doi.org/10.1016/j.jpeds.2014.06.065

rvals over 50 ms

quared differences between adjacent normal RR intervals

1

Table I. General characteristics of adolescents(n = 1152)

Variables Values

Age (y) 16.6 � 1.2Weight (kg) 63.7 � 12.6Height (cm) 171.6 � 7.1WC (cm) 76.6 � 9.4BMI (kg/m2) 21.6 � 3.8SBP (mm Hg) 121.6 � 12.4DBP (mm Hg) 67.8 � 8.6Race (% non-whites) 72.0Place of residence (% urban) 79.2Abdominal obesity (%) 15.4Overweight (%) 16.6High blood pressure (%) 9.7Physical inactivity (%) 64.4

BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; WC, waistcircumference.

THE JOURNAL OF PEDIATRICS � www.jpeds.com Vol. -, No. -

Public School System in the State of Pernambuco (northeastBrazil). Volunteers with known diabetes mellitus, cardiovas-cular disease, and neurologic or mental disabilities wereexcluded. Exclusion criteria also included consumption ofcaffeinated beverages 12 hours prior to the HRV evaluation;use of alcohol, any form of tobacco, and/or other illicit drugs;and participation in any physical exercise training 24 hoursbefore evaluations.

Data collection was performed between May and Octoberin 2011 and the period of the day that the adolescents were inclass (morning, afternoon, and evening). Age, ethnic back-ground, place of residence, and physical activity level wereobtained using the Global School-based Student Health Sur-vey, as proposed by the World Health Organization forsimilar epidemiologic studies in children and adolescents,which is available at www.who.int/chp/gshs/en. Physical ac-tivity level was assessed by the question “During the past7 days on how many days were you physically active for a to-tal of at least 60 minutes per day?” Adolescents were classifiedas active (if the answer was 5 or more days per week with atleast 60 minutes per day of moderate to vigorous physical ac-tivity) or physically inactive.20 Reproducibility indicators (ie,test retest consistency, 1-week apart) ranged from moderateto high for the majority of the items, with kappa coefficientof 0.77 for physical activity level.

Table II. Mean � SD and percentile values for HRV parame

Parameters Mean ± SD 1 5 10

RR interval (ms) 827 � 128 571 637 670 7SDNN (ms) 61.9 � 23.7 21.3 29.4 34.9RMSSD (ms) 54.5 � 29.4 10.1 18.2 22.6PNN50 (%) 29.4 � 20.4 0.0 1.10 3.0Variance (ms2) 3992 � 3138 411 792 1090 18LF (ms2) 1268 � 1024 94 242 349 6HF (ms2) 1377 � 1424 51 127 201 4LF/HF 1.44 � 1.08 0.23 0.36 0.47LF (nu) 53.0 � 15.6 18.9 26.7 32.2HF (nu) 47.0 � 15.6 15.7 22.3 26.2

2

Adolescents were weighed without shoes and coats on anautomatic scale, and the height was measured using a stadiom-eter.Waist circumferencewasmeasured in the standingpositionat the level of the umbilicus using a constant tension tape. Over-weight was determined by body mass index above the 85thpercentile for their age.21 Abdominal obesity was determinedby waist circumference above the 80th percentile for their age.22

Blood pressure was measured using the Omron HEM74223 (Omron, Shangai, China) after the adolescents restedand remained seated with legs uncrossed for 5 minutes.Appropriate cuff size was used for each adolescent. All bloodpressure measurements were performed 3 times in the rightarm placed at heart level in a seated position. The mean valueof the last 2 measurements was used for analysis. High bloodpressure was defined as systolic and/or diastolic blood pres-sure equal or higher than the reference sex-, age-, andheight-specific 95th percentile.24

HRV was assessed from the RR intervals obtained by aheart rate monitor (POLAR, RS 800CX; Polar Electro OyInc, Kempele, Finland). Adolescents remained in the supineposition for 10 minutes, after approximately 30 minutes atrest. All analyses were performed with Kubios HRV software(Biosignal Analysis and Medical Imaging Group, Joensuu,Finland) by a single evaluator blinded to the other study vari-ables, following the recommendations of the Task Force ofthe European Society of Cardiology and the North AmericanSociety of Pacing and Electrophysiology.2

The time-domain variables, such as SD of all RR intervals(SDNN), root mean square of the squared differences be-tween adjacent normal RR intervals (RMSSD), and the per-centage of adjacent intervals over 50 ms (PNN50) wereobtained. The frequency-domain variables were analyzed us-ing the spectral analysis of HRV. Stationary periods of the ta-chogram, with at least 5 minutes, were broken down intobands of low (LF) and high (HF) frequencies, using the au-toregressive method with a fixed model order of 12. Fre-quencies between 0.04 and 0.4 Hz were considered asphysiologically significant, where the LF component was rep-resented by oscillations between 0.04 and 0.15 Hz andHF wasrepresented by oscillations between 0.15 and 0.4 Hz. The po-wer of each spectral component was normalized by dividingthe power of each spectrum band by the total variance, minusthe value of very low frequency band (<0.04 Hz), and

ters in adolescents (n = 1152)

Percentile

25 50 75 90 95 99

36 815 905 997 1062 116544.5 58.4 76.5 92.9 103.7 138.733.2 49.6 68.9 94.2 113.0 153.611.0 28.1 45.1 58.3 66.2 75.049 3092 5388 7687 9821 17 22214 990 1644 2465 3023 535722 937 1801 3219 4137 72320.71 1.14 1.80 2.82 3.49 5.3741.6 53.2 64.3 73.8 77.7 84.335.7 46.8 58.4 67.8 73.3 81.1

Farah et al

Table

III.

Relationship

betweenHRVparam

etersandWC,BMI,SB

P,DBP,andPAin

adolescents(n

=1152)

RRinterval(m

s)SDNN(m

s)RMSSD

(ms)

PNN50

(%)

LF/HF

LF(nu)

HF(nu)

b(SE)

bb(SE)

bb(SE)

bb(SE)

bb(SE)

bb(SE)

bb(SE)

b

WC(cm)

�0.85(0.41)

�0.06*

�0.12(0.07)

�0.05

�0.23(0.09)

�0.07

�0.20(0.07)

�0.09*

0.010(0.003)

0.09*

0.18

(0.05)

0.11*

�0.18(0.05)

�0.11*

BMI(kg/m

2)

�0.11(1.01)

�0.003

�0.14(0.19)

�0.02

�0.34(0.23)

�0.04

�0.29(0.16)

�0.05

0.022(0.009)

0.08

0.41

(0.12)

0.10*

�0.41(0.12)

�0.10*

SBP(mmHg)

�2.17(0.31)

�0.21*

�0.24(0.06)

�0.13*

�0.43(0.07)

�0.18*

�0.32(0.05)

�0.19*

0.014(0.003)

0.17*

0.24

(0.38)

0.19*

�0.24(0.38)

�0.19*

DBP(mmHg)

�3.60(0.43)

�0.24*

�0.34(0.08)

�0.13*

�0.54(0.10)

�0.16*

�0.43(0.07)

�0.18*

0.014(0.004)

0.11*

0.21

(0.05)

0.10*

�0.21(0.05)

�0.10*

PA(d/wk)

7.60

(1.64)

0.14*

1.14

(0.30)

0.11*

1.54

(0.38)

0.12*

1.02

(0.26)

0.11*

�0.03(0.01)

�0.05

�0.56(0.20)

�0.08*

0.56

(0.20)

0.08*

b,regression

coefficient;b,standard

coefficient;PA,physicalactivity.

Adjustedforageandtim

eofday*P

<.05.

- 2014 ORIGINAL ARTICLES

Heart Rate Variability and Cardiovascular Risk Factors in Adolesc

multiplying the result by 100. To interpret the results, the LFand HF normalized components of the HRV were consid-ered, respectively, as representative of predominantly sympa-thetic and parasympathetic modulation of the heart, and theratio between these bands (LF/HF) was defined as the cardiacsympathovagal balance.2 In a subsample of 27 adolescents,the reliability of HRV measures was assessed after 1 week.Intraclass correlation coefficient ranged from 0.68-0.91.

Statistical AnalysesAll statistical analyses were performed using SPSS/PASW v 20(IBM Corp, Armonk, New York). All HRV variables weredescribed as mean � SD and percentiles. Multiple linearregression analysis was conducted to examine the relation-ship between HRV measures and waist circumference, bodymass index, blood pressure, and physical activity, adjustedfor age and the period of the day (morning, afternoon andevening). A residual analysis was performed, and for eachmodel the assumption of adherence to the normal distribu-tion was followed.Clustering of RFs was considered to be the sum of insuffi-

cient level of physical activity, abdominal obesity, and highblood pressure. A 1-way ANOVA, followed by the Tukeypost hoc test was used to compare HRV measures with thenumber of cardiovascular RFs (0, 1, and 2 ormore). A P valueof <.05 was considered statistically significant.

Results

A total of 1212 boys were enrolled in the study; 60 boys wereexcluded due to low signal quality (stationary periods of thetachogram length lower than 5minutes). Thus, the final anal-ysis consists of data from 1152 boys with a mean age of16.6 � 1.2 years old. The general characteristics of adoles-cents are presented in Table I. Among the participants,28.5% had no abnormal cardiovascular RFs, and 56.7%and 14.9% showed 1 RF and 2 or more RFs, respectively.Values of different components of HRV in the participants

along with percentiles of the HRV variables in the cohort areshown in Table II.Multiple linear regression analysis between HRV variables

andwaist circumference, bodymass index, systolic and diastolicblood pressure, and physical activity are presented in Table III.Waist circumference was negatively correlated with RR interval(P = .037) and PNN50 (P = .002). Body mass index wasnegatively correlated with HF (P = .001) and positively withLF (P = .001). Systolic and diastolic blood pressures werenegatively correlated with RR interval, SDNN, RMSSD,PNN50, and HF (P < .001 for all measures) and positivelywith LF and LF/HF (P < .001). Physical activity was positivelycorrelated with RR interval, SDNN, RMSSD, PNN50, and HF(P < .001 for all measures) and negatively with LF (P < .001).The Figure summarizes the influence of clustered

cardiovascular RFs on HRV variables. For all HRVvariables, the subjects with 2 or more RFs ($2 RF)presented lower values than the subject with no RFs (0 RF),except for the LF and LF/HF in which subjects with 2 RFs

ent Boys 3

Figure. Comparison of HRVmeasures according to the number of RFs in adolescents (n = 1147).White bars: 0 RF, black bars: 1RF; gray bars: $2RFs. *Difference from 0 RF. zDifference from 1 RF. P < .05. Values are presented in mean and SD.

THE JOURNAL OF PEDIATRICS � www.jpeds.com Vol. -, No. -

presented higher values than 0 RF (P < .001). In addition, theRR interval, SDNN, RMSSD, and PNN50 were lower foradolescents with 1 RF compared with 0 RF (P < .001).

Discussion

The present study establishes reference values for the HRVmeasures in the time and frequency domains in a large sam-ple of adolescent boys. Similar studies that performed anal-ysis of time and frequency domains data, as well as theduration of signal acquisition (at least 5 minutes) have shownmedian values of SDNN (67 vs 58ms), RMSSD (73 vs 50ms),PNN50 (41% vs 28%), and HF (62 vs 58 nu) in children,which are higher than that found in the present study.

4

However, LF (38 vs 53 nu) and LF/HF (0.62 vs 1.14) valueswere lower.9 Interestingly, lower median values for SDNN(36.4 vs 61.9 ms) and RMSSD (25.8 vs 54.5 ms), and higherLF/HF (1.70 vs 1.44) were observed in 65-year-old individ-uals10 compared with the corresponding values in the adoles-cents in the current study. This linear trend in increasing thesympathetic modulation while decreasing the parasympa-thetic modulation to the heart during the transition fromchildhood to adulthood may suggest a progressive evolutionof the autonomic nervous system during this period. Further,this may reflect the potential for the increased risk of adversecardiovascular outcomes in the aging process.Similar to the well described data in adults,25-27 our results

indicated that abdominal obesity was related with more HRV

Farah et al

- 2014 ORIGINAL ARTICLES

variables compared with that of overweight status alone.Indeed, individuals with abdominal obesity typically haveincreased insulin resistance and are likely to have inflamma-tion, compared with individuals with excess weightonly.18,19,28,29 In the same way, the level of physical activitywas related to lower sympathetic modulation and increasedparasympathetic modulation in adolescents. Gutin et al13 re-ported an association between moderate to vigorous physicalactivity levels and higher HRV in adolescents. In addition,increased parasympathetic modulation is correlated withhigher cardiorespiratory fitness in adolescents.13 Therefore,it is likely that adolescents at a higher physical activity levelhave higher cardiorespiratory fitness as displayed by higherHRV observed in the present study.

The results indicated that adolescent boys who displayedgreater sympathetic and lower parasympathetic modulationat rest hadhigherbloodpressure.These data support a previoussmaller study,15 which reported a trend toward higher sympa-thetic and lower parasympatheticmeasures in adolescentmaleswith higher blood pressure. In the current study, multipleregression analysis showed that among the RFs included thestrongest association was between systolic blood pressure andmeasures of HRV. A low HRV is associated with high bloodpressure mainly because of the increase in cardiac output as aresult of higher heart rate1 and a decrease in baroreflex sensi-tivity, commonly observed in patients with hypertesion.30,31

However, it is likely that other mechanisms are involved inthe observed relationship and the elevated blood pressure.

More than 14% of the adolescent boys presented with 2 ormore RFs, and they showed markedly lower HRV comparedwith those with none or 1 RF. Previous studies in childrenand adults also found a similar relationship between theincreased number of components of metabolic syndromeand lower parasympathetic modulation at rest.17,32 A novelobservation of the present study is that clustering of tradi-tional RFs resulted in a worsening of HRV profile in a dosedependent manner.

A strength of our study is clearly the large sample size. Wetried to control for various potential confounders in thestudy. HRV was analyzed by a single researcher in a blindedfashion. Because race may impact HRV determinations andpotential heterogeneity exists within and between Brazilianpopulations/groups,33 our data need to be interpretedcautiously and may not be generalizable to all adolescents.The cross-sectional design and the correlative nature of thedata preclude us from establishing a causal relationship be-tween HRV variables and RFs. Although the participants’ages were tightly controlled, we could not determine the Tan-ner stage of the participants. Similarly, we were not able toinclude the measurement of cardiorespiratory fitness, insulinresistance, and markers of inflammation in this study. Longi-tudinal studies are warranted to understand the underlyingmechanisms responsible for the associations observed inthe current study. n

Submitted for publication Mar 25, 2014; last revision received May 27, 2014;

accepted Jun 27, 2014.

Heart Rate Variability and Cardiovascular Risk Factors in Adolesc

Reprint requests: Raphael M. Ritti-Dias, PhD, School of Physical Education,

Pernambuco University, 310, Arn�obio Marques Str Recife, PE 50100-130,

Brazil. E-mail: raphaelritti@gmail.com

References

1. Malpas SC. Sympathetic nervous system overactivity and its role in the

development of cardiovascular disease. Physiol Rev 2010;90:513-57.

2. Heart rate variability. Standards of measurement, physiological interpre-

tation, and clinical use. Task Force of the European Society of Cardiol-

ogy and the North American Society of Pacing and Electrophysiology.

Eur Heart J 1996;17:354-81.

3. Kotecha D, New G, Flather MD, Eccleston D, Pepper J, Krum H. Five-

minute heart rate variability can predict obstructive angiographic coro-

nary disease. Heart 2012;98:395-401.

4. Rodrigues LB, Miranda AS, Lima AH, Forjaz CL, Wolosker N, Ritti-

Dias RM. Sympathetic cardiac modulation and vascular worsening in

arteritis: a case report. J Vasc Nurs 2012;30:21-3.

5. Jaiswal M, Urbina EM, Wadwa RP, Talton JW, D’agostino RB Jr,

Hamman RF, et al. Reduced heart rate variability is associated with

increased arterial stiffness in youth with type 1 diabetes: the SEARCH

CVD study. Diabetes Care 2013;36:2351-8.

6. Singh JP, LarsonMG, Tsuji H, Evans JC, O’donnell CJ, Levy D. Reduced

heart rate variability and new-onset hypertension: insights into patho-

genesis of hypertension: the Framingham Heart Study. Hypertension

1998;32:293-7.

7. Schroeder EB, Liao D, Chambless LE, Prineas RJ, Evans GW, Heiss G.

Hypertension, blood pressure, and heart rate variability: the Athero-

sclerosis Risk in Communities (ARIC) study. Hypertension 2003;42:

1106-11.

8. Jaiswal M, Urbina EM, Wadwa RP, Talton JW, D’agostino RB Jr,

Hamman RF, et al. Reduced heart rate variability among youth with

type 1 diabetes: the SEARCH CVD study. Diabetes Care 2013;36:157-62.

9. Michels N, Clays E, De Buyzere M, Huybrechts I, Marild S, Vanaelst B,

et al. Determinants and reference values of short-term heart rate vari-

ability in children. Eur J Appl Physiol 2013;113:1477-88.

10. Kim GM, Woo JM. Determinants for heart rate variability in a normal

Korean population. J Korean Med Sci 2011;26:1293-8.

11. Riva P, Martini G, Rabbia F, Milan A, Paglieri C, Chiandussi L, et al.

Obesity and autonomic function in adolescence. Clin Exp Hypertens

2001;23:57-67.

12. Rabbia F, Silke B, Conterno A, Grosso T, De Vito B, Rabbone I, et al.

Assessment of cardiac autonomicmodulation during adolescent obesity.

Obes Res 2003;11:541-8.

13. Gutin B, Howe C, Johnson MH, Humphries MC, Snieder H, Barbeau P.

Heart rate variability in adolescents: relations to physical activity, fitness,

and adiposity. Med Sci Sports Exerc 2005;37:1856-63.

14. Henje Blom E, Olsson EM, Serlachius E, EricsonM, IngvarM. Heart rate

variability is related to self-reported physical activity in a healthy adoles-

cent population. Eur J Appl Physiol 2009;106:877-83.

15. Urbina EM, BaoW, Pickoff AS, Berenson GS. Ethnic (black-white) con-

trasts in heart rate variability during cardiovascular reactivity testing in

male adolescents with high and low blood pressure: the Bogalusa Heart

Study. Am J Hypertens 1998;11:196-202.

16. Farah BQ, Do Prado WL, Tenorio TR, Ritti-Dias RM. Heart rate vari-

ability and its relationship with central and general obesity in obese

normotensive adolescents. Einstein (Sao Paulo) 2013;11:285-90.

17. Zhou Y, Xie G, Wang J, Yang S. Cardiovascular risk factors significantly

correlate with autonomic nervous system activity in children. Can J Car-

diol 2012;28:477-82.

18. Balagopal PB, De Ferranti SD, Cook S, Daniels SR, Gidding SS,

Hayman LL, et al. Nontraditional risk factors and biomarkers for cardio-

vascular disease: mechanistic, research, and clinical considerations for

youth: a scientific statement from the American Heart Association. Cir-

culation 2011;123:2749-69.

19. Steinberger J, Daniels SR, Eckel RH, Hayman L, Lustig RH, Mccrindle B,

et al. Progress and challenges in metabolic syndrome in children and ad-

olescents: a scientific statement from the American Heart Association

ent Boys 5

THE JOURNAL OF PEDIATRICS � www.jpeds.com Vol. -, No. -

Atherosclerosis, Hypertension, and Obesity in the Young Committee of

the Council on Cardiovascular Disease in the Young; Council on Cardio-

vascular Nursing; and Council on Nutrition, Physical Activity, and

Metabolism. Circulation 2009;119:628-47.

20. Cavill N, Bidlle S, Sallis J. Health enhancing physical activity for young

people: statement of United Kingdom expert consensus conference. Pe-

diatr Exerc Sci 2001;13:12-25.

21. Cole TJ, Bellizzi MC, Flegal KM, DietzWH. Establishing a standard defi-

nition for child overweight and obesity worldwide: international survey.

BMJ 2000;320:1240-3.

22. Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist

circumference, waist-to-hip ratio, and the conicity index as screening

tools for high trunk fat mass, as measured by dual-energy X-ray absorp-

tiometry, in children aged 3-19 y. Am J Clin Nutr 2000;72:490-5.

23. Christofaro DG, Fernandes RA, Gerage AM, Alves MJ, Polito MD,

Oliveira AR. Validation of the Omron HEM 742 blood pressure moni-

toring device in adolescents. Arq Bras Cardiol 2009;92:10-5.

24. Falkner B, Daniels SR. Summary of the Fourth Report on the Diagnosis,

Evaluation, and Treatment of High Blood Pressure in Children and Ad-

olescents. Hypertension 2004;44:387-8.

25. Yi SH, Lee K, Shin DG, Kim JS, Kim HC. Differential association of

adiposity measures with heart rate variability measures in Koreans. Yon-

sei Med J 2013;54:55-61.

6

26. Chen GY, Hsiao TJ, Lo HM, Kuo CD. Abdominal obesity is associated

with autonomic nervous derangement in healthy Asian obese subjects.

Clin Nutr 2008;27:212-7.

27. Windham BG, Fumagalli S, Ble A, Sollers JJ, Thayer JF, Najjar SS, et al.

The relationship between heart rate variability and adiposity differs for

central and overall adiposity. J Obes 2012;2012:149516.

28. Davy KP, Hall JE. Obesity and hypertension: two epidemics or one? Am J

Physiol Regul Integr Comp Physiol 2004;286:R803-13.

29. Thiyagarajan R, Subramanian SK, Sampath N, Madanmohan T, Pal P,

Bobby Z, et al. Association between cardiac autonomic function, oxida-

tive stress and inflammatory response in impaired fasting glucose sub-

jects: cross-sectional study. PLoS One 2012;7:e41889.

30. Honzikova N, Fiser B. Baroreflex sensitivity and essential hypertension

in adolescents. Physiol Res 2009;58:605-12.

31. Genovesi S, Pieruzzi F,GiussaniM,TonoV, Stella A,PortaA, et al. Analysis

of heart period and arterial pressure variability in childhood hypertension:

key role of baroreflex impairment. Hypertension 2008;51:1289-94.

32. Min KB, Min JY, Paek D, Cho SI. The impact of the components of

metabolic syndrome on heart rate variability: using the NCEP-ATP III

and IDF definitions. Pacing Clin Electrophysiol 2008;31:584-91.

33. Guerreiro-Junior V, Bisso-Machado R, Marrero A, Hunemeier T,

Salzano FM, Bortolini MC. Genetic signatures of parental contribution

in black and white populations in Brazil. Genet Mol Biol 2009;32:1-11.

Farah et al