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Baroreflex sensitivity is reduced in adolescents with probable developmental coordination disorder
Research in Developmental Disabilities 33 (2012) 251–257
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Research in Developmental Disabilities
Baroreflex sensitivity is reduced in adolescents with probable developmental coordination disorder Nicole S. Coverdale a, Deborah D. O’Leary a,*, Brent E. Faught a, Daniele Chirico a, John Hay a, John Cairney b a b
Department of Community Health Sciences, Brock University, 550 Glenridge Ave., St Catharines, ON, Canada L2S 3A1 Departments of Family Medicine, Psychiatry, and Behavioural Neurosciences & Kinesiology, McMaster University, 1280 Main St W, Hamilton, ON, Canada L8S 4L8
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 September 2011 Accepted 12 September 2011 Available online 11 October 2011
Developmental coordination disorder (DCD) is a neurodevelopmental condition characterized by poor motor skills leading to a significant impairment in activities of daily living. Compared to typically developing children, those with DCD are less fit and physically active, and have increased body fat. This is an important consequence as both sedentary lifestyle and obesity are risk factors for cardiovascular disease. One indicator of cardiovascular health is baroreflex sensitivity (BRS), which is a measure of short term blood pressure (BP) regulation and is partly accomplished through changes in heart rate. Diminished BRS is predictive of future cardiovascular morbidity and mortality. The purpose of this study was to compare BRS in typically developing (TD) adolescents with probable DCD (pDCD) or suspect pDCD (spDCD) adolescents (13–14 years of age). Percentile scores on the Movement Assessment Battery for Children, 2nd edition, assessed at two time points were averaged and used to classify participants into the following groups: pDCD 5th percentile, spDCD > 5th percentile and 16th percentile and TD > 16th percentile. Following 15 min of supine rest, 5 min of continuous beat-by-beat blood pressure (Finapres) and R–R interval were recorded (standard ECG). Spectral indices were computed using Fast Fourier Transform with transfer function analysis used to compute BRS in the low frequency region (0.04–0.15 Hz). BRS was compared between groups with an ANOVA and post hoc Bonferroni correction. BRS was reduced in the pDCD compared to the TD groups. In multivariate regression analyses predicting BRS, when pDCD and spDCD were entered as the only variables, pDCD was found to be a significant predictor of BRS (b = 6.74, p = 0.016). However, when sex, VO2 peak, and percent body fat (PBF) were entered as covariates, pDCD was no longer a predictor, while PBF approached significance (0.32, p = 0.056). Therefore, in this sample, BRS was reduced in adolescents with pDCD principally due to increased PBF. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Developmental Coordination Disorder Baroreflex sensitivity Obesity Adolescents
1. Introduction Developmental coordination disorder (DCD) is a neuro-developmental condition characterized by deficits in fine and/or gross motor coordination (American Psychiatric Association, 2000). To establish a diagnosis of DCD, these motor coordination difficulties must significantly interfere with activities of daily living or academic achievement in the absence of a pre-existing medical or neurological condition (American Psychiatric Association, 2000). Prevalence estimates vary between 1.8% and 5% based on how stringently the diagnostic criteria are applied (Lingam, Hunt, Golding, Jongmans, & Emond, 2009).
* Corresponding author. Tel.: +1 905 688 5550x4339; fax: +1 905 688 8954. E-mail address: [email protected] (D.D. O’Leary). 0891-4222/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ridd.2011.09.013
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Motor coordination difficulties may affect many aspects of a child’s life including their participation in both free and organized play (Cairney et al., 2005). For example, when children with movement difficulties were observed during school recess periods, these children participated less in vigorous activity than their peers without movement difficulties (Bouffard, Watkinson, Thompson, Causgrove Dunn, & Romanow, 1996). Similar findings using accelerometry data for children aged 12– 13 years have demonstrated that children with probable DCD (pDCD) are less active than their typically developing peers (Silman, Cairney, Hay, Klentrou, & Faught, 2011). As well, a longitudinal investigation examining data collected at five time points over 30 months, showed that children with pDCD consistently participated in less free and organized play over that time span (Cairney, Hay, Veldhuizen, Missiuna, & Faught, 2010). In addition to decreased participation in physical activity, children with DCD also have lower levels of fitness and increased fat mass compared to typically developing peers. Fitness levels have been examined using field tests such as the Leger 20-m shuttle run and with laboratory measures to establish peak aerobic power (VO2 peak). Using either methodology, levels of fitness are consistently lower in children with pDCD than their typically developing peers (Faught, Hay, Cairney, & Flouris, 2005; Silman et al., 2011; Wu, Lin, Li, Tsai, & Cairney, 2010). Additionally, using whole body air-displacement plethysmography to evaluate body composition children with pDCD display increased fat mass, but similar lean mass to that of typically developing peers (Cairney, Hay, Veldhuizen, & Faught, 2011). The findings that children with DCD participate less in physical activity, have reduced fitness levels, and increased fat mass are concerning, as these are all risk factors for cardiovascular disease. For example, childhood obesity tracks into adulthood and is associated with an increase in arterial stiffness (Raitakari, Juonala, & Viikari, 2005), which is a surrogate marker of atherosclerosis. In addition, both decreased fitness and physical activity levels are associated with cardiovascular risk factors such as blood pressure (BP), fasting glucose levels, and cholesterol levels in children (Ekelund et al., 2007) and with all-cause mortality and heart attack risk in adults (Blair et al., 1989; Lakka et al., 1994). Therefore, it is necessary to more closely examine the cardiovascular health of children with DCD. The autonomic nervous system (ANS) is an important regulator of BP and heart rate. The ANS is composed of parasympathetic and sympathetic branches that act antagonistically to regulate BP around a set point (Berne & Levy, 1997). When BP changes, arterial baroreceptors, located in the carotid sinus and aortic arch, are triggered and in turn send a neural signal to the brain stem where parasympathetic and/or sympathetic activity is altered. Parasympathetic nervous system activation decreases heart rate, while sympathetic activation increases heart rate and peripheral vasoconstriction (Berne & Levy, 1997). For example, when BP increases, baroreceptors trigger parasympathetic activation, along with sympathetic deactivation, resulting in a decrease in BP. One way to evaluate autonomic function is to measure heart rate variability (HRV). In healthy individuals, heart rate or R– R interval (RRI) varies within a specific range of frequencies typically grouped in terms of high frequency (HF: 0.15–0.40 Hz) and low frequency (LF: 0.04–0.15 Hz) (Berntson et al., 1997). High frequency fluctuations represent parasympathetic activity, while LF fluctuations represent a combination of sympathetic and parasympathetic activity (Berntson et al., 1997). Additionally, the ratio of LF to HF (LF/HF) is often used as a measure of sympathovagal balance (Berntson et al., 1997). Decreased total HRV and HF power and increased LF power are associated with cardiovascular morbidity and mortality (Guzzetti et al., 1988; Hayano et al., 1991; Tsuji et al., 1994). In addition, baroreflex sensitivity (BRS; ms/mm Hg), defined as the change in RRI for a change in BP, is another measure of autonomic regulation. In fact, decreased BRS has been found to be indicative of cardiovascular morbidity and mortality (La Rovere, Bigger, Marcus, Mortara, & Schwartz, 1998; Mortara et al., 1997). It is fairly well established that BRS is reduced with obesity in adults and adolescents (Beske, Alvarez, Ballard, & Davy, 2002; Lazarova et al., 2009; Skrapari et al., 2007). As well, the age-associated decline in BRS can be attenuated with aerobic exercise (Monahan et al., 2000), while elderly people with higher levels of fitness have higher BRS than sedentary elderly adults (Shi et al., 2008). The impact of fitness and/or physical activity on BRS in children and adolescents has received little research attention. However, Gutin et al. (2005) found that higher levels of fitness and amount of moderate-vigorous physical activity were associated with parasympathetic modulation as measured with HRV. The purpose of this study was to examine BRS in adolescents with pDCD. It was hypothesized that adolescents with pDCD would display lower BRS compared to typically developing peers as a result of lower fitness levels and a higher percentage of body fat. 2. Methods 2.1. Sample The sample for this study was drawn from a population-based study that examined motor coordination and physical health known as PHAST (Physical Health and Activity Study Team), the details of which have been described in detail elsewhere (Cairney, Hay, Veldhuizen, & Faught, 2010; Cairney, Hay, Veldhuizen, Missiuna, et al., 2010). Initially, all adolescents who scored below the 10th percentile on the Bruininks–Oseretsky test of motor proficiency short form during school testing were contacted and 63 agreed to take part in the study. Following this, 63 adolescents who scored at or above the 10th percentile were matched to those already recruited for age, sex, and school location (total n = 126). This study uses data from the second year of lab-based testing. After the first year of testing, 21 participants declined the invitation to participate in the study for a second year, resulting in a sample size of 105. The Brock University research ethics board and
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the District School Board of Niagara approved this study. Additionally a parent or guardian provided informed consent for each adolescent. 2.2. Experimental protocol Upon arrival at the laboratory, parents/guardians signed the consent form. Following this, height and weight were collected and body composition was determined. Participants then began cardiovascular testing. They were asked to lie supine for a period of 15 min to allow BP and HR to stabilize to resting levels. Three manual BPs were then collected. Beat-bybeat collection of BP and RRI then began and continued for 5 min in a dark and quiet setting. Three manual BPs were also taken following beat-by-beat collection. After completion of cardiovascular testing the participants completed the Movement Assessment Battery for Children, 2nd edition (M-ABC-2) with the occupational therapist, followed by the VO2 peak test. 2.3. Measures 2.3.1. Body composition Height was measured (cm) with a stadiometer (STAT 7X, Ellard Instrumentation Ltd., Monroe, WA, USA) and body mass was measured (kg) with a digital scale (BWB-800S, Tanita Digital Scale, Tokyo, Japan) after the removal of shoes and excessive clothing (i.e. sweaters, coats, etc.). Body mass index (BMI) was calculated as weight over height squared (kg/m2). Air-displacement plethysmography was used to evaluate body composition. The BOD POD (Life Measurement Inc., Concord, CA, USA) gives similar estimates of body composition as hydrodensitometry in children (Nunez et al., 1999). To achieve an accurate body fat estimate with the BOD POD it was important that subjects wear tight fitting clothes to reduce excess volume, thus subjects wore swim suits or a spandex uni-suit and a swim cap during measurement. Two measurements were taken and averaged to estimate body composition from which percent body fat was calculated as fat mass divided by total mass multiplied by one hundred. 2.3.2. Blood pressure and heart rate Beat-by-beat BP was collected with a Finapres (Ohmeda 2300, Finapres Medical Systems, Arnhem, Netherlands) on the middle finger of the left hand positioned at heart level and RRI with a standard single lead electrocardiogram. Before and after data collection three manual BP measures were performed on the right brachial artery using a stethoscope and sphygmomanometer. Because BP taken at the finger slightly differs from that taken at the arm, the average of three manual BPs taken at the beginning of beat-by-beat recording were used to adjust the beat-by-beat values collected simultaneously by the Finapres (Imholz, Settels, van der Meiracker, Wesseling, & Wieling, 1990). The primary purpose for the measurement of manual BP was to serve as a reference point for the beat-by-beat BP data, while the secondary purpose was to ensure that the subject remained at rest throughout the protocol. Both BP and RRI were sampled at a rate of 1000 Hz, providing a basic resolution of one millisecond. These beat-by-beat data were then used for HRV and BRS analysis. 2.3.3. Developmental coordination disorder Motor coordination was assessed during the lab visit using the M-ABC-2 (Henderson, Sugden, & Barnett, 2007). The MABC-2 was administered by a trained occupational therapist. The M-ABC-2 examines fine and gross motor coordination and contains eight tasks grouped into three categories that include manual dexterity, aiming, catching and balance. Children who score below the 5th percentile have significant motor coordination difficulties and may require intervention, while children who score at or below the 16th percentile have motor difficulties and should be followed to determine if intervention is necessary (Henderson et al., 2007). Since neither activities of daily living or school achievement were examined for this study (American Psychiatric Association, 2000), the term probable or ‘‘pDCD’’ was used to classify the adolescents. M-ABC-2 percentile scores from year one and year two of lab testing were averaged and used to classify adolescents as pDCD if they scored at or below the 5th percentile, as suspect pDCD (spDCD) if they scored above the 5th percentile and less than or equal to the 16th percentile, and typically developing (TD) if they scored greater than the 16th percentile. 2.3.4. Aerobic fitness VO2 peak was determined on a programmed cycle ergometer (Excalibur Sport V2, Lode BV, Groningen, Netherlands). A continuous, incremental protocol was used. A mouthpiece with nose clips was used to collect metabolic gases and participants had a chance to familiarize themselves with the equipment before the protocol began. Metabolic gases were analyzed using an AIE metabolic cart (Model S-3A, AIE Technologies, Pittsburgh, PA, USA). Heart rate was recorded continuously during the assessment. As well, in order to account for body size, relative VO2 peak was determined by normalizing for body mass (mL/kg). Participants began cycling at 60–65 rpm with an initial power output of 20 W for the first 3 min. After this point, power output was increased by 20 W every minute until the final stages, at which point power output was increased by 15 W every minute until volitional fatigue. To verify that the subject had reached peak aerobic power, two of the three following criteria were met: HR >85% of the predicted maximum value (220-age), respiratory exchange ratio greater than 1.00, physical signs
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of effort and fatigue (facial reddening, difficulty keeping up with the speed of the cycle) causing the participants to volitionally stop. 2.3.5. Pubertal maturation Maturation was self-reported by participants. Pictures that represent each Tanner stage of development were taken from Taylor et al. (2001) and participants were asked to report which picture most resembled them (Taylor et al., 2001). This was completed in the home in the presence of the participant’s parent or guardian to reduce embarrassment. 2.4. Cardiovascular analysis Average RRI was calculated as the average time from R-peak of the ECG to the next R-peak (ms) during the last minute of the 5 min period of data collection. Average SBP and DBP were calculated as the average value from the maximum of the Finapres BP curve and the minimum, respectively, during the last minute of data collection. Beat-by-beat data was saved for offline analysis. Using the 2007 version of Microsoft Excel the data was scanned to ensure that it was free from ectopic beats. The same researcher analyzed all files in order to eliminate inter-observer variability. As is common practice, beat-by-beat RRI and SBP were de-trended, re-sampled using the mean cardiac frequency to obtain an equal interval between samples, and a low-pass Butterworth filter was used at 0.95 Hz. Fast Fourier transform was then completed with the Welch method (Welch, 1967) and Hanning window, with the window size set to one-fourth of the signal length with one-half overlap (Livingstone, Peralta-Huertas, Phillips, Klentrou, & O’Leary, 2010; O’Leary, Shoemaker, Edwards, & Hughson, 2004). LF area was set to 0.04–0.15 Hz and HF area to 0.15–0.4 Hz for all variables, while total power represented the sum of LF and HF. BRS was determined using transfer function analysis for the LF region (0.04–0.15 Hz) with coherence 0.5 (O’Leary et al., 2004). This method for obtaining BRS in children has been previously published (Lenard, Studinger, Mersich, Kocsis, & Kollai, 2004; Livingstone et al., 2010). 2.5. Statistical analysis An ANOVA was used to compare the three groups (pDCD, spDCD, and TD) for all demographic, cardiovascular, HRV, and BRS variables with a Bonferroni post hoc test to determine differences between groups. Analyses were completed with SPSS version 18.0 (SPSS Inc., Chicago, IL) and level of significance was p 0.05. Sex and Tanner stages for breast/penis development and pubic hair were compared between groups with a chi square test. Additionally, a multivariate regression was performed to examine the effect of pDCD on BRS, controlling for percent body fat (PDF), VO2 peak, and sex. These variables were added to the regression since each may affect autonomic function (Dietrich et al., 2006; Gutin et al., 2005; Lazarova et al., 2009). 3. Results 3.1. Sample characteristics There were no differences between the TD, spDCD, and pDCD groups for sex or maturational stage (breast/penis development and pubic hair) (Table 1). By design, adolescents with spDCD and pDCD had lower M-ABC-2 percentile scores than TD adolescents (p < 0.001 for each comparison). Adolescents in the TD group had lower weight and BMI compared to the spDCD group (p = 0.003 for weight and BMI) and the pDCD group (p = 0.003 for weight and p < 0.001 for BMI). Typically Table 1 Demographic and cardiovascular measures in TD, spDCD and pDCD adolescents.
Age Sex (n = male, n = female) M-ABC-2 percentile Height (m) Weight (kg) BMI (kg/m2) RRI (ms) SBP (mm Hg) DBP (mm Hg) PBF (%) VO2 peak (mL/kg)
TD
spDCD
pDCD
13.2 0.4 29, 23 47 17 162.9 6.5 54.7 11.2 20.6 3.7 858 120 108 9 61 7 19.3 9.9 45.8 8.8
13.3 0.6 12, 10 11 3y 164.9 8.3 68.1 19.4y 24.8 5.8y 828 106 111 21 63 7 29.5 9.5y 36.0 7.3y
13.4 0.5 14, 7 3 2* 162.6 8.2 68.6 20.2* 25.7 6.58* 812 94 113 9 67 7* 32.1 10.6* 36.1 8.7*
Mean standard deviation. Groups compared with ANOVA and Bonferroni post hoc test. TD = typically developing, pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder, M-ABC2 = Movement Assessment Battery for Children, 2nd edition, BMI = body mass index, RRI = R-R interval, SBP = systolic blood pressure, DBP = diastolic blood pressure, PBF = percent body fat, VO2 peak = peak aerobic fitness. * p 0.05 for TD versus pDCD. y p 0.05 for TD versus spDCD.
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Table 2 HRV and BRS measures in TD, spDCD and pDCD adolescents.
2
LF (ms ) HF (ms2) TP (ms2) LF/HF BRS (ms/mm Hg)
TD
spDCD
pDCD
755 593 1208 1389 1955 1828 0.91 0.57 19.7 11.5
703 450 1019 935 1722 1277 1.07 0.71 17.8 10.2
503 328 621 609 1124 889 1.34 1.09 13.0 5.2*
Mean standard deviation. Groups compared with ANOVA and Bonferroni post hoc test. TD = typically developing, pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder, LF = low frequency, HF = high frequency, TP = total power, BRS = baroreflex sensitivity. * p 0.05 for TD versus pDCD.
Table 3 Regression of pDCD on BRS with covariates.
pDCD spDCD Sex PBF (%) VO2 peak (mL/kg) Constant Adjusted R2
Model 1
p-Value
Model 2
p-Value
6.74 (1.43) 1.93 (2.61)
0.016 0.462
3.18 (3.21) 0.845 (2.94) 0.24 (2.09) 0.32 (0.17) 0.06 (0.21) 18.2 0.13
0.324 0.774 0.920 0.056 0.790
19.7 0.06
Values are unstandardized b-coefficients with standard error of the coefficient in parentheses. pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder PBF = percent body fat, VO2 peak = peak aerobic fitness.
developing adolescents had lower DBP than the pDCD group (p = 0.005). Additionally, the spDCD and pDCD groups had increased PBF compared to the TD group (p < 0.001 for each comparison), while the TD group had higher relative VO2 peak (mL/kg) values than spDCD and pDCD groups (p < 0.001 for each comparison). 3.2. Cardiovascular measurements For HRV measures, LF, HF, TP and the LF/HF ratio were not different between groups (Table 2). Alternatively, BRS was lower in the pDCD compared to the TD group (p = 0.049). As differences were only observed between subjects in the pDCD group, a variable was created comparing these participants to all other participants in the sample (spDCD and TD controls) for use in multivariate analyses. When pDCD was entered into a model with BRS as the dependent variable, pDCD was a significant negative predictor of BRS (p = 0.016) (Table 3); notably, pDCD status alone accounted for 6% of the total variation in BRS. However, after sex, PBF, and VO2 peak were entered into the model, pDCD was no longer significant. In this model, PBF as a significant predictor of BRS was approaching significance (p = 0.056). Together, all the variables in model 2 (Table 3) accounted for 13% of the variation in BRS in the sample. Interactions between each covariate (sex, PBF, VO2 peak) and pDCD were created and entered into the full model. None of the interactions reached the threshold for statistical significance. 4. Discussion The purpose of this study was to determine if BRS was reduced in a sample of participants with pDCD as a result of decreased fitness levels and increased body fatness. It was determined that BRS was lower in the pDCD group and that this was mainly attributed to higher PBF in those with pDCD. As well, though not significantly different between groups, the HRV findings of lower HF and TP and higher LF/HF ratio in the pDCD group suggest decreased parasympathetic and increased sympathetic variability. These findings agree with previous results among obese and/or inactive children and adolescents (Gutin et al., 2005; Riva et al., 2001). We also found evidence of a dose–response relationship between motor coordination and BRS; children who scored at or below the 5th percentile were lowest in BRS, relative to those with more moderate coordination problems and typically developing adolescents. A similar finding for body fat has been observed in this population (Cairney et al., 2011). Several studies have shown that BRS is attenuated in obese children and adolescents (Dangardt et al., 2011; Lazarova et al., 2009). Various hypotheses have been suggested to explain how obesity decreases baroreflex function but they are incompletely understood. The baroreflex loop is composed of a mechanical component as the baroreceptors respond to stretch of the vessel wall when BP changes. There is also a neural component to the baroreflex loop. The deformation of the vessel wall initiates a neural signal that travels to the nucleus tractus solitarus of the brain via the cranial nerves, which leads to alteration of the parasympathetic and sympathetic branches (Hunt, Fahy, Farquhar, & Taylor, 2001). Therefore, alterations in either branch of the baroreflex loop could result in decreased BRS. It is possible that obese adolescents have reduced BRS as a result of increased arterial stiffness, hence the mechanical component. Obesity in childhood is associated with carotid
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artery stiffness in adulthood and this process of arterial stiffening likely begins in childhood (Juonala et al., 2005). As well, obese children have greater carotid artery stiffness (Banach et al., 2010; Tounian et al., 2001). Lower carotid artery stiffness, or increased elasticity, means that the vessel wall is able to stretch more during a change in BP, thus influencing the size of the stimulus to alter heart rate. If stiffness is increased then the vessel wall is more rigid and cannot stretch as greatly to initiate the baroreflex loop. Obesity may also alter the neural component of baroreflex function indirectly through alterations in insulin resistance and leptin. It is well documented that obesity is associated with insulin resistance (Tascilar et al., 2011) and Muscelli et al. (1998) demonstrated that euglycemic hyperinsulinemia was associated with decreased HRV and a shift from parasympathetic to sympathetic control of heart rate (Muscelli et al., 1998), which is associated with reduced BRS (Tank et al., 2004). As well, levels of the hormone leptin increase proportionally with obesity. Animal studies have demonstrated that increased leptin levels in obese rats are associated with maladaptive cardiovascular responses such as increased sympathetic nerve activity to the kidney and adrenal glands, and increased BP (Rahmouni, 2010). It is likely that a combination of the mechanisms discussed above serve to decrease BRS in obese children and adults. A limitation to this study is that these subjects have not been fully diagnosed with DCD, as there was no independent measure of activities of daily living or academic achievement. As well, this study is limited by the fact that this examination was cross-sectional. Though we are proposing the pathway of decreased motor coordination leading to increased body fat and in turn altered autonomic cardiovascular control, it is possible that obesity is the first step in this pathway. In this case, it could be argued that obese adolescents are less likely to participate in physical activity, resulting in the lack of development of motor coordination. A longitudinal study starting with very young children would be necessary to establish a definite timeline. 5. Conclusion This is the first study to demonstrate reduced BRS in pDCD compared to TD adolescents and analyses indicate that this difference is a result of increased PBF in adolescents with pDCD. Since reduced BRS is a risk factor for future cardiovascular disease, a longitudinal investigation is necessary to determine whether cardiovascular morbidity and mortality is increased in this population. 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Contents lists available at SciVerse ScienceDirect
Research in Developmental Disabilities
Baroreflex sensitivity is reduced in adolescents with probable developmental coordination disorder Nicole S. Coverdale a, Deborah D. O’Leary a,*, Brent E. Faught a, Daniele Chirico a, John Hay a, John Cairney b a b
Department of Community Health Sciences, Brock University, 550 Glenridge Ave., St Catharines, ON, Canada L2S 3A1 Departments of Family Medicine, Psychiatry, and Behavioural Neurosciences & Kinesiology, McMaster University, 1280 Main St W, Hamilton, ON, Canada L8S 4L8
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 September 2011 Accepted 12 September 2011 Available online 11 October 2011
Developmental coordination disorder (DCD) is a neurodevelopmental condition characterized by poor motor skills leading to a significant impairment in activities of daily living. Compared to typically developing children, those with DCD are less fit and physically active, and have increased body fat. This is an important consequence as both sedentary lifestyle and obesity are risk factors for cardiovascular disease. One indicator of cardiovascular health is baroreflex sensitivity (BRS), which is a measure of short term blood pressure (BP) regulation and is partly accomplished through changes in heart rate. Diminished BRS is predictive of future cardiovascular morbidity and mortality. The purpose of this study was to compare BRS in typically developing (TD) adolescents with probable DCD (pDCD) or suspect pDCD (spDCD) adolescents (13–14 years of age). Percentile scores on the Movement Assessment Battery for Children, 2nd edition, assessed at two time points were averaged and used to classify participants into the following groups: pDCD 5th percentile, spDCD > 5th percentile and 16th percentile and TD > 16th percentile. Following 15 min of supine rest, 5 min of continuous beat-by-beat blood pressure (Finapres) and R–R interval were recorded (standard ECG). Spectral indices were computed using Fast Fourier Transform with transfer function analysis used to compute BRS in the low frequency region (0.04–0.15 Hz). BRS was compared between groups with an ANOVA and post hoc Bonferroni correction. BRS was reduced in the pDCD compared to the TD groups. In multivariate regression analyses predicting BRS, when pDCD and spDCD were entered as the only variables, pDCD was found to be a significant predictor of BRS (b = 6.74, p = 0.016). However, when sex, VO2 peak, and percent body fat (PBF) were entered as covariates, pDCD was no longer a predictor, while PBF approached significance (0.32, p = 0.056). Therefore, in this sample, BRS was reduced in adolescents with pDCD principally due to increased PBF. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Developmental Coordination Disorder Baroreflex sensitivity Obesity Adolescents
1. Introduction Developmental coordination disorder (DCD) is a neuro-developmental condition characterized by deficits in fine and/or gross motor coordination (American Psychiatric Association, 2000). To establish a diagnosis of DCD, these motor coordination difficulties must significantly interfere with activities of daily living or academic achievement in the absence of a pre-existing medical or neurological condition (American Psychiatric Association, 2000). Prevalence estimates vary between 1.8% and 5% based on how stringently the diagnostic criteria are applied (Lingam, Hunt, Golding, Jongmans, & Emond, 2009).
* Corresponding author. Tel.: +1 905 688 5550x4339; fax: +1 905 688 8954. E-mail address: [email protected] (D.D. O’Leary). 0891-4222/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ridd.2011.09.013
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Motor coordination difficulties may affect many aspects of a child’s life including their participation in both free and organized play (Cairney et al., 2005). For example, when children with movement difficulties were observed during school recess periods, these children participated less in vigorous activity than their peers without movement difficulties (Bouffard, Watkinson, Thompson, Causgrove Dunn, & Romanow, 1996). Similar findings using accelerometry data for children aged 12– 13 years have demonstrated that children with probable DCD (pDCD) are less active than their typically developing peers (Silman, Cairney, Hay, Klentrou, & Faught, 2011). As well, a longitudinal investigation examining data collected at five time points over 30 months, showed that children with pDCD consistently participated in less free and organized play over that time span (Cairney, Hay, Veldhuizen, Missiuna, & Faught, 2010). In addition to decreased participation in physical activity, children with DCD also have lower levels of fitness and increased fat mass compared to typically developing peers. Fitness levels have been examined using field tests such as the Leger 20-m shuttle run and with laboratory measures to establish peak aerobic power (VO2 peak). Using either methodology, levels of fitness are consistently lower in children with pDCD than their typically developing peers (Faught, Hay, Cairney, & Flouris, 2005; Silman et al., 2011; Wu, Lin, Li, Tsai, & Cairney, 2010). Additionally, using whole body air-displacement plethysmography to evaluate body composition children with pDCD display increased fat mass, but similar lean mass to that of typically developing peers (Cairney, Hay, Veldhuizen, & Faught, 2011). The findings that children with DCD participate less in physical activity, have reduced fitness levels, and increased fat mass are concerning, as these are all risk factors for cardiovascular disease. For example, childhood obesity tracks into adulthood and is associated with an increase in arterial stiffness (Raitakari, Juonala, & Viikari, 2005), which is a surrogate marker of atherosclerosis. In addition, both decreased fitness and physical activity levels are associated with cardiovascular risk factors such as blood pressure (BP), fasting glucose levels, and cholesterol levels in children (Ekelund et al., 2007) and with all-cause mortality and heart attack risk in adults (Blair et al., 1989; Lakka et al., 1994). Therefore, it is necessary to more closely examine the cardiovascular health of children with DCD. The autonomic nervous system (ANS) is an important regulator of BP and heart rate. The ANS is composed of parasympathetic and sympathetic branches that act antagonistically to regulate BP around a set point (Berne & Levy, 1997). When BP changes, arterial baroreceptors, located in the carotid sinus and aortic arch, are triggered and in turn send a neural signal to the brain stem where parasympathetic and/or sympathetic activity is altered. Parasympathetic nervous system activation decreases heart rate, while sympathetic activation increases heart rate and peripheral vasoconstriction (Berne & Levy, 1997). For example, when BP increases, baroreceptors trigger parasympathetic activation, along with sympathetic deactivation, resulting in a decrease in BP. One way to evaluate autonomic function is to measure heart rate variability (HRV). In healthy individuals, heart rate or R– R interval (RRI) varies within a specific range of frequencies typically grouped in terms of high frequency (HF: 0.15–0.40 Hz) and low frequency (LF: 0.04–0.15 Hz) (Berntson et al., 1997). High frequency fluctuations represent parasympathetic activity, while LF fluctuations represent a combination of sympathetic and parasympathetic activity (Berntson et al., 1997). Additionally, the ratio of LF to HF (LF/HF) is often used as a measure of sympathovagal balance (Berntson et al., 1997). Decreased total HRV and HF power and increased LF power are associated with cardiovascular morbidity and mortality (Guzzetti et al., 1988; Hayano et al., 1991; Tsuji et al., 1994). In addition, baroreflex sensitivity (BRS; ms/mm Hg), defined as the change in RRI for a change in BP, is another measure of autonomic regulation. In fact, decreased BRS has been found to be indicative of cardiovascular morbidity and mortality (La Rovere, Bigger, Marcus, Mortara, & Schwartz, 1998; Mortara et al., 1997). It is fairly well established that BRS is reduced with obesity in adults and adolescents (Beske, Alvarez, Ballard, & Davy, 2002; Lazarova et al., 2009; Skrapari et al., 2007). As well, the age-associated decline in BRS can be attenuated with aerobic exercise (Monahan et al., 2000), while elderly people with higher levels of fitness have higher BRS than sedentary elderly adults (Shi et al., 2008). The impact of fitness and/or physical activity on BRS in children and adolescents has received little research attention. However, Gutin et al. (2005) found that higher levels of fitness and amount of moderate-vigorous physical activity were associated with parasympathetic modulation as measured with HRV. The purpose of this study was to examine BRS in adolescents with pDCD. It was hypothesized that adolescents with pDCD would display lower BRS compared to typically developing peers as a result of lower fitness levels and a higher percentage of body fat. 2. Methods 2.1. Sample The sample for this study was drawn from a population-based study that examined motor coordination and physical health known as PHAST (Physical Health and Activity Study Team), the details of which have been described in detail elsewhere (Cairney, Hay, Veldhuizen, & Faught, 2010; Cairney, Hay, Veldhuizen, Missiuna, et al., 2010). Initially, all adolescents who scored below the 10th percentile on the Bruininks–Oseretsky test of motor proficiency short form during school testing were contacted and 63 agreed to take part in the study. Following this, 63 adolescents who scored at or above the 10th percentile were matched to those already recruited for age, sex, and school location (total n = 126). This study uses data from the second year of lab-based testing. After the first year of testing, 21 participants declined the invitation to participate in the study for a second year, resulting in a sample size of 105. The Brock University research ethics board and
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the District School Board of Niagara approved this study. Additionally a parent or guardian provided informed consent for each adolescent. 2.2. Experimental protocol Upon arrival at the laboratory, parents/guardians signed the consent form. Following this, height and weight were collected and body composition was determined. Participants then began cardiovascular testing. They were asked to lie supine for a period of 15 min to allow BP and HR to stabilize to resting levels. Three manual BPs were then collected. Beat-bybeat collection of BP and RRI then began and continued for 5 min in a dark and quiet setting. Three manual BPs were also taken following beat-by-beat collection. After completion of cardiovascular testing the participants completed the Movement Assessment Battery for Children, 2nd edition (M-ABC-2) with the occupational therapist, followed by the VO2 peak test. 2.3. Measures 2.3.1. Body composition Height was measured (cm) with a stadiometer (STAT 7X, Ellard Instrumentation Ltd., Monroe, WA, USA) and body mass was measured (kg) with a digital scale (BWB-800S, Tanita Digital Scale, Tokyo, Japan) after the removal of shoes and excessive clothing (i.e. sweaters, coats, etc.). Body mass index (BMI) was calculated as weight over height squared (kg/m2). Air-displacement plethysmography was used to evaluate body composition. The BOD POD (Life Measurement Inc., Concord, CA, USA) gives similar estimates of body composition as hydrodensitometry in children (Nunez et al., 1999). To achieve an accurate body fat estimate with the BOD POD it was important that subjects wear tight fitting clothes to reduce excess volume, thus subjects wore swim suits or a spandex uni-suit and a swim cap during measurement. Two measurements were taken and averaged to estimate body composition from which percent body fat was calculated as fat mass divided by total mass multiplied by one hundred. 2.3.2. Blood pressure and heart rate Beat-by-beat BP was collected with a Finapres (Ohmeda 2300, Finapres Medical Systems, Arnhem, Netherlands) on the middle finger of the left hand positioned at heart level and RRI with a standard single lead electrocardiogram. Before and after data collection three manual BP measures were performed on the right brachial artery using a stethoscope and sphygmomanometer. Because BP taken at the finger slightly differs from that taken at the arm, the average of three manual BPs taken at the beginning of beat-by-beat recording were used to adjust the beat-by-beat values collected simultaneously by the Finapres (Imholz, Settels, van der Meiracker, Wesseling, & Wieling, 1990). The primary purpose for the measurement of manual BP was to serve as a reference point for the beat-by-beat BP data, while the secondary purpose was to ensure that the subject remained at rest throughout the protocol. Both BP and RRI were sampled at a rate of 1000 Hz, providing a basic resolution of one millisecond. These beat-by-beat data were then used for HRV and BRS analysis. 2.3.3. Developmental coordination disorder Motor coordination was assessed during the lab visit using the M-ABC-2 (Henderson, Sugden, & Barnett, 2007). The MABC-2 was administered by a trained occupational therapist. The M-ABC-2 examines fine and gross motor coordination and contains eight tasks grouped into three categories that include manual dexterity, aiming, catching and balance. Children who score below the 5th percentile have significant motor coordination difficulties and may require intervention, while children who score at or below the 16th percentile have motor difficulties and should be followed to determine if intervention is necessary (Henderson et al., 2007). Since neither activities of daily living or school achievement were examined for this study (American Psychiatric Association, 2000), the term probable or ‘‘pDCD’’ was used to classify the adolescents. M-ABC-2 percentile scores from year one and year two of lab testing were averaged and used to classify adolescents as pDCD if they scored at or below the 5th percentile, as suspect pDCD (spDCD) if they scored above the 5th percentile and less than or equal to the 16th percentile, and typically developing (TD) if they scored greater than the 16th percentile. 2.3.4. Aerobic fitness VO2 peak was determined on a programmed cycle ergometer (Excalibur Sport V2, Lode BV, Groningen, Netherlands). A continuous, incremental protocol was used. A mouthpiece with nose clips was used to collect metabolic gases and participants had a chance to familiarize themselves with the equipment before the protocol began. Metabolic gases were analyzed using an AIE metabolic cart (Model S-3A, AIE Technologies, Pittsburgh, PA, USA). Heart rate was recorded continuously during the assessment. As well, in order to account for body size, relative VO2 peak was determined by normalizing for body mass (mL/kg). Participants began cycling at 60–65 rpm with an initial power output of 20 W for the first 3 min. After this point, power output was increased by 20 W every minute until the final stages, at which point power output was increased by 15 W every minute until volitional fatigue. To verify that the subject had reached peak aerobic power, two of the three following criteria were met: HR >85% of the predicted maximum value (220-age), respiratory exchange ratio greater than 1.00, physical signs
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of effort and fatigue (facial reddening, difficulty keeping up with the speed of the cycle) causing the participants to volitionally stop. 2.3.5. Pubertal maturation Maturation was self-reported by participants. Pictures that represent each Tanner stage of development were taken from Taylor et al. (2001) and participants were asked to report which picture most resembled them (Taylor et al., 2001). This was completed in the home in the presence of the participant’s parent or guardian to reduce embarrassment. 2.4. Cardiovascular analysis Average RRI was calculated as the average time from R-peak of the ECG to the next R-peak (ms) during the last minute of the 5 min period of data collection. Average SBP and DBP were calculated as the average value from the maximum of the Finapres BP curve and the minimum, respectively, during the last minute of data collection. Beat-by-beat data was saved for offline analysis. Using the 2007 version of Microsoft Excel the data was scanned to ensure that it was free from ectopic beats. The same researcher analyzed all files in order to eliminate inter-observer variability. As is common practice, beat-by-beat RRI and SBP were de-trended, re-sampled using the mean cardiac frequency to obtain an equal interval between samples, and a low-pass Butterworth filter was used at 0.95 Hz. Fast Fourier transform was then completed with the Welch method (Welch, 1967) and Hanning window, with the window size set to one-fourth of the signal length with one-half overlap (Livingstone, Peralta-Huertas, Phillips, Klentrou, & O’Leary, 2010; O’Leary, Shoemaker, Edwards, & Hughson, 2004). LF area was set to 0.04–0.15 Hz and HF area to 0.15–0.4 Hz for all variables, while total power represented the sum of LF and HF. BRS was determined using transfer function analysis for the LF region (0.04–0.15 Hz) with coherence 0.5 (O’Leary et al., 2004). This method for obtaining BRS in children has been previously published (Lenard, Studinger, Mersich, Kocsis, & Kollai, 2004; Livingstone et al., 2010). 2.5. Statistical analysis An ANOVA was used to compare the three groups (pDCD, spDCD, and TD) for all demographic, cardiovascular, HRV, and BRS variables with a Bonferroni post hoc test to determine differences between groups. Analyses were completed with SPSS version 18.0 (SPSS Inc., Chicago, IL) and level of significance was p 0.05. Sex and Tanner stages for breast/penis development and pubic hair were compared between groups with a chi square test. Additionally, a multivariate regression was performed to examine the effect of pDCD on BRS, controlling for percent body fat (PDF), VO2 peak, and sex. These variables were added to the regression since each may affect autonomic function (Dietrich et al., 2006; Gutin et al., 2005; Lazarova et al., 2009). 3. Results 3.1. Sample characteristics There were no differences between the TD, spDCD, and pDCD groups for sex or maturational stage (breast/penis development and pubic hair) (Table 1). By design, adolescents with spDCD and pDCD had lower M-ABC-2 percentile scores than TD adolescents (p < 0.001 for each comparison). Adolescents in the TD group had lower weight and BMI compared to the spDCD group (p = 0.003 for weight and BMI) and the pDCD group (p = 0.003 for weight and p < 0.001 for BMI). Typically Table 1 Demographic and cardiovascular measures in TD, spDCD and pDCD adolescents.
Age Sex (n = male, n = female) M-ABC-2 percentile Height (m) Weight (kg) BMI (kg/m2) RRI (ms) SBP (mm Hg) DBP (mm Hg) PBF (%) VO2 peak (mL/kg)
TD
spDCD
pDCD
13.2 0.4 29, 23 47 17 162.9 6.5 54.7 11.2 20.6 3.7 858 120 108 9 61 7 19.3 9.9 45.8 8.8
13.3 0.6 12, 10 11 3y 164.9 8.3 68.1 19.4y 24.8 5.8y 828 106 111 21 63 7 29.5 9.5y 36.0 7.3y
13.4 0.5 14, 7 3 2* 162.6 8.2 68.6 20.2* 25.7 6.58* 812 94 113 9 67 7* 32.1 10.6* 36.1 8.7*
Mean standard deviation. Groups compared with ANOVA and Bonferroni post hoc test. TD = typically developing, pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder, M-ABC2 = Movement Assessment Battery for Children, 2nd edition, BMI = body mass index, RRI = R-R interval, SBP = systolic blood pressure, DBP = diastolic blood pressure, PBF = percent body fat, VO2 peak = peak aerobic fitness. * p 0.05 for TD versus pDCD. y p 0.05 for TD versus spDCD.
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Table 2 HRV and BRS measures in TD, spDCD and pDCD adolescents.
2
LF (ms ) HF (ms2) TP (ms2) LF/HF BRS (ms/mm Hg)
TD
spDCD
pDCD
755 593 1208 1389 1955 1828 0.91 0.57 19.7 11.5
703 450 1019 935 1722 1277 1.07 0.71 17.8 10.2
503 328 621 609 1124 889 1.34 1.09 13.0 5.2*
Mean standard deviation. Groups compared with ANOVA and Bonferroni post hoc test. TD = typically developing, pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder, LF = low frequency, HF = high frequency, TP = total power, BRS = baroreflex sensitivity. * p 0.05 for TD versus pDCD.
Table 3 Regression of pDCD on BRS with covariates.
pDCD spDCD Sex PBF (%) VO2 peak (mL/kg) Constant Adjusted R2
Model 1
p-Value
Model 2
p-Value
6.74 (1.43) 1.93 (2.61)
0.016 0.462
3.18 (3.21) 0.845 (2.94) 0.24 (2.09) 0.32 (0.17) 0.06 (0.21) 18.2 0.13
0.324 0.774 0.920 0.056 0.790
19.7 0.06
Values are unstandardized b-coefficients with standard error of the coefficient in parentheses. pDCD = probable developmental coordination disorder, spDCD = suspect probable developmental coordination disorder PBF = percent body fat, VO2 peak = peak aerobic fitness.
developing adolescents had lower DBP than the pDCD group (p = 0.005). Additionally, the spDCD and pDCD groups had increased PBF compared to the TD group (p < 0.001 for each comparison), while the TD group had higher relative VO2 peak (mL/kg) values than spDCD and pDCD groups (p < 0.001 for each comparison). 3.2. Cardiovascular measurements For HRV measures, LF, HF, TP and the LF/HF ratio were not different between groups (Table 2). Alternatively, BRS was lower in the pDCD compared to the TD group (p = 0.049). As differences were only observed between subjects in the pDCD group, a variable was created comparing these participants to all other participants in the sample (spDCD and TD controls) for use in multivariate analyses. When pDCD was entered into a model with BRS as the dependent variable, pDCD was a significant negative predictor of BRS (p = 0.016) (Table 3); notably, pDCD status alone accounted for 6% of the total variation in BRS. However, after sex, PBF, and VO2 peak were entered into the model, pDCD was no longer significant. In this model, PBF as a significant predictor of BRS was approaching significance (p = 0.056). Together, all the variables in model 2 (Table 3) accounted for 13% of the variation in BRS in the sample. Interactions between each covariate (sex, PBF, VO2 peak) and pDCD were created and entered into the full model. None of the interactions reached the threshold for statistical significance. 4. Discussion The purpose of this study was to determine if BRS was reduced in a sample of participants with pDCD as a result of decreased fitness levels and increased body fatness. It was determined that BRS was lower in the pDCD group and that this was mainly attributed to higher PBF in those with pDCD. As well, though not significantly different between groups, the HRV findings of lower HF and TP and higher LF/HF ratio in the pDCD group suggest decreased parasympathetic and increased sympathetic variability. These findings agree with previous results among obese and/or inactive children and adolescents (Gutin et al., 2005; Riva et al., 2001). We also found evidence of a dose–response relationship between motor coordination and BRS; children who scored at or below the 5th percentile were lowest in BRS, relative to those with more moderate coordination problems and typically developing adolescents. A similar finding for body fat has been observed in this population (Cairney et al., 2011). Several studies have shown that BRS is attenuated in obese children and adolescents (Dangardt et al., 2011; Lazarova et al., 2009). Various hypotheses have been suggested to explain how obesity decreases baroreflex function but they are incompletely understood. The baroreflex loop is composed of a mechanical component as the baroreceptors respond to stretch of the vessel wall when BP changes. There is also a neural component to the baroreflex loop. The deformation of the vessel wall initiates a neural signal that travels to the nucleus tractus solitarus of the brain via the cranial nerves, which leads to alteration of the parasympathetic and sympathetic branches (Hunt, Fahy, Farquhar, & Taylor, 2001). Therefore, alterations in either branch of the baroreflex loop could result in decreased BRS. It is possible that obese adolescents have reduced BRS as a result of increased arterial stiffness, hence the mechanical component. Obesity in childhood is associated with carotid
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artery stiffness in adulthood and this process of arterial stiffening likely begins in childhood (Juonala et al., 2005). As well, obese children have greater carotid artery stiffness (Banach et al., 2010; Tounian et al., 2001). Lower carotid artery stiffness, or increased elasticity, means that the vessel wall is able to stretch more during a change in BP, thus influencing the size of the stimulus to alter heart rate. If stiffness is increased then the vessel wall is more rigid and cannot stretch as greatly to initiate the baroreflex loop. Obesity may also alter the neural component of baroreflex function indirectly through alterations in insulin resistance and leptin. It is well documented that obesity is associated with insulin resistance (Tascilar et al., 2011) and Muscelli et al. (1998) demonstrated that euglycemic hyperinsulinemia was associated with decreased HRV and a shift from parasympathetic to sympathetic control of heart rate (Muscelli et al., 1998), which is associated with reduced BRS (Tank et al., 2004). As well, levels of the hormone leptin increase proportionally with obesity. Animal studies have demonstrated that increased leptin levels in obese rats are associated with maladaptive cardiovascular responses such as increased sympathetic nerve activity to the kidney and adrenal glands, and increased BP (Rahmouni, 2010). It is likely that a combination of the mechanisms discussed above serve to decrease BRS in obese children and adults. A limitation to this study is that these subjects have not been fully diagnosed with DCD, as there was no independent measure of activities of daily living or academic achievement. As well, this study is limited by the fact that this examination was cross-sectional. Though we are proposing the pathway of decreased motor coordination leading to increased body fat and in turn altered autonomic cardiovascular control, it is possible that obesity is the first step in this pathway. In this case, it could be argued that obese adolescents are less likely to participate in physical activity, resulting in the lack of development of motor coordination. A longitudinal study starting with very young children would be necessary to establish a definite timeline. 5. Conclusion This is the first study to demonstrate reduced BRS in pDCD compared to TD adolescents and analyses indicate that this difference is a result of increased PBF in adolescents with pDCD. Since reduced BRS is a risk factor for future cardiovascular disease, a longitudinal investigation is necessary to determine whether cardiovascular morbidity and mortality is increased in this population. 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