Assessment of Growth in Pharmacological Treatment-Naïve Polish Boys with Attention-Deficit/Hyperactivity Disorder

Abstract

Objective:

The objective of this study was to estimate the growth of pharmacological treatment-naïve polish boys with attention-deficit/hyperactivity disorder (ADHD).

Method:

The sample included 135 boys (mean age: 11.67 years) with ADHD. The level of subjects' height, weight, and body mass index (BMI) was compared to the reference growth charts. Full estimation of measurement accuracy was provided. Regression analysis was used to estimate the biological and social factors contributing to the growth determination in the examined group.

Results:

There were no statistically significant differences between mean body height of boys with ADHD and standards of growth of Polish children. Separate analyses for body height of the examined boys aged 6–10, 11–15, and 16–18 years also gave no statistically significant results. Mean body weight (z=0.28) and BMI (z=0.25) in the total cohort were statistically higher than the norm. After categorization of the boys according to age, statistically significant differences were demonstrated only for weight in the age range of 6–10 years (z=0.31) and for BMI in the age range of 11–15y (z=0.42). The regression analysis showed the strongest relation between the subjects' growth and the parents' body size, newborn's condition (birth, body weight, and APGAR score), factors connected with lifestyle, and socio-economic status of the family.

Conclusion:

The study revealed that the height of drug-naïve boys with ADHD was not significantly different from the norm. The investigation also showed a tendency for greater body weight and BMI in boys with ADHD in comparison with the growth charts, which may be manifested also in greater risk of overweight and obesity in this group. The results of research suggest the necessity to control for such variables as genetic, perinatal, socioeconomic, and psychosocial factors, which may affect children's development, in future research on the growth of children with ADHD.

Introduction

T he influence of pharmacotherapy on the growth of children with attention-deficit/ hyperactivity disorder (ADHD) has been repeatedly investigated since Safer et al. (1972) published their first observations. After four decades of research (Satterfield et al. 1979; Rao et al. 1998, Biederman et al. 2003; Lisska and Rivkees 2003; Poulton and Cowell 2003; Bereket et al. 2005; Spencer et al. 2005; Swanson et al. 2006; Zachor et al. 2006; Faraone and Giefer 2007; Poulton and Nanan 2009), the findings are a matter of controversy. However, there is evidence that stimulants can slow down the growth rate.

Comprehensive reviews and meta-analyses of the growth of children with ADHD as well as of the influence of drugs on the process, can be found in the works of Poulton (2005) and Ptáček et al. (2009a). Poulton (2005) reviewed 29 studies; 11 of them supported the suppressing effect of the drugs. The other studies, according to the authors, were based on research on samples that were too small, or they had other methodological issues. A similar review was conducted by Ptáček et al. (2009a). Among the 23 articles included, 15 indicated the occurrence of growth slowdown or loss of body weight as a result of using stimulants.

Several studies have suggested that the observed growth deviation could be related to ADHD itself (Spencer et al. 1996; Hanć and Cieślik 2008a,b; Ptáček et al. 2009c). However, evidence relating to this is scare because there are not many studies of growth of children with ADHD who had no pharmacological treatment. The methods of measuring and assessment of growth have generally not been described in detail. For example, information on intra- and inter-observer errors are usually not given. Therefore, we do not know if the anthropometric measurements were conducted with enough accuracy to enable a reliable assessment of growth.

The main aim of the present study was to estimate the growth of pharmacological treatment-naïve children with ADHD.

Methods

Sample

The research was conducted in the years 2004–2008. The subjects were recruited from 13 mental health clinics and psychological and pedagogical counseling centers located in 7 Polish cities (Poznań, Warszawa, Kraków, Gdańsk, Gdynia, Szczecin, and Lublin). The sample consisted of boys who had never been treated with any psychotropic drug. The diagnosis of ADHD was made by trained psychiatrists or psychologists and confirmed by using Diagnostic Interview for ADHD and Hyperkinetic Disorder (Wolańczyk and Kokakowski, 2005). This methodology has previously been shown to give 90% concordance (Hanć and Cieślik 2008b). The exclusion criteria were thyroid dysfunction, organic brain dysfunction, epilepsy, fetal alcohol syndrome, and mental retardation. Only boys from whom a signed assent was obtained and whose legal guardians signed a consent form were included in the sample. The study was approved by the Ethics Committee of the Poznań University of Medical Sciences (decision number: 855/05). The research received financial support from the Polish Ministry of Science and Higher Education (grant number: N N303 0175 33).

Study design

The study was cross-sectional. The medical history of the subjects was collected using a structured interview with the parents and information obtained from medical records. The interview also included information on variables that may have affected the growth process: 1) socioeconomic status of the family, assessed using parents' level of education, number of household members per room in the household, type of accommodation, income level evaluation; 2) pregnancy and birth conditions: type of pregnancy (normal or other, e.g., singleton or multiple, prematurity of<37 weeks), birth body weight, newborn condition assessed using the Apgar score, delivery type (normal or not), breastfeeding for ≥6 months; 3) psychosocial life conditions: family type (with both biological parents or other, e.g., reconstructed, single-parent, adopted). These characteristics are presented in Table 1.

Table 1.

The Basic Characterization of the Sample

Variables	Categories	n	%
Basic information about the family
Level of parents' education	Both with higher education	36	26.67
	One with higher education	41	30.37
	Both with education below higher	58	42.96
Income level	Low level	18	13.33
	Medium level	65	48.15
	High level	52	38.52
Place of residence	Villages	12	8.89
	Small towns	12	8.89
	Medium-size cities	17	12.59
	Large cities	94	69.63
Family type	With both biological parents	95	70.37
	Other	40	29.63
Pregnancy, birth and newborn condition
Type of pregnancy	Normal	102	75.56
	Complicated	33	24.44
Delivery type	On time	120	88.89
	Premature	15	11.11
Apgar score (first minute)	>4	3	2.22
	4–6	5	3.70
	7–8	22	16.30
	9–10	97	71.85
Breastfeeding	no	29	21.48
	yes	106	78.52

Height and weight measurements

The growth measurements (height and weight) were taken by trained physical anthropologists using a Gneupel Praezision Mechanik (GPM) anthropometer with measurement accuracy amounting to±1 mm. The measurements were performed according to the standard technique (Onis de et al. 2004; Chumlea and Guo 2006): The children were standing upright, head established in the Frankfurt plane passing through the orbital point (the inferior margin of the orbit) and the porion point (the upper margin of the ear canal) parallel to the surface of the Earth. Body height was defined as the distance between the basis (ground) and the vertex (the highest skull point when the head is established in the Frankfurt plane). Boys' weight was measured using the medical weight machine with measurement accuracy of±100 g. The children were assessed wearing only underwear. Most measurements were taken from 16.00 to 18.00 o'clock (n=83, 61%). The time of measurements for the rest of the children ranged from 9.00 to 15.00 o'clock.

Two replicate measurements were taken at the same opportunity and for each subject the arithmetic means were used.

Body mass index (BMI) was calculated as the weight in kg divided by twice the height in meters.

Assessment of measurement accuracy

In order to estimate the intra-observer technical error of present measurements (intra-TEM), the inter-observer technical error (inter-TEM), and the reliability of measurements and the average bias, a special session was organized. The observers (n=5) had previously been trained in the anthropometry technique by an expert. Ten boys with ADHD were randomly chosen to participate in the accuracy evaluation. Each child was measured twice by the expert and twice by the trained observers. This assessment used the methods recommended by the World Health Organization (WHO) (WHO Multicentre Growth Reference Study Group 2006).

Data categorization and standardization

The references growth data were provided by the Institute of Mother and Child in Warsaw and are recommended by the Polish Ministry of Health (Palczewska and Niedźwiecka 2001). These data give standard measurements at intervals of 1 year, therefore for calculating the z scores we assumed a constant rate of change of the mean and standard deviation for height, weight, and BMI between one year and the next.

The cohort was subdivided into three age ranges: 6–10, 11–15, and 16–18 years. These categories were chosen on the assumption that the pubertal growth spurt occurred in the age range 11–15 years. It was assumed that younger children were prepubertal, and in the boys>15 years of age the pubertal growth spurt was over. However, we had no clinical data to confirm this.

Statistical analysis

The difference between the z scores and the reference data was estimated using single sample Student's t test (standardized against an expected mean of 0 and standard deviation of 1). The three age ranges were compared using analysis of variance (ANOVA).

The forward stepwise regression method was used to build the multifactorial models of growth determination of children with ADHD. The analyses were conducted for the subgroup (n=127), for which we had complete data. The z scores for height, weight, and BMI were used as dependent variables. The independent variables tested related to demography, socioeconomic status, physical health, and psychological factors, potentially affecting children`s growth (basic characteristics of the study sample are presented in Table 1). The correctness of models fitting into the empirical data was estimated using the variance analysis. The criterion of model approval was the statistical significance of F value on the level of p<0.05. The Durbin–Watson test was used to detect the presence of autocorrelation in the residuals. The impact of collinearity between the variables in the regression model was estimated using the variance inflation factor (VIF). VIF value>10 was chosen as an indicator of multicollinearity and exclusion criterion for variables. Statistica 8 software was used for all the statistical analyses. Differences were considered significant when p<0.05. All tests were two-tailed.

Results

A total of 150 treatment-naïve polish boys with ADHD were considered for the study. Fifteen were excluded: Seven did not consent (three children and four parents) and eight met other exclusion criteria. The final study sample included 135 boys aged 6–18 years, mean age: 11.67; standard deviation: 3.92; age categories: 6–10 years (82), 11–15 years (30), and 16–18 years (23).

Most subjects came from vast urbanized areas (70%), were raised in families with two biological parents (70%), and had at least one parent with tertiary education (43%). The parents most frequently declared average (48%) or high (39%) income. The majority of the boys had been born after pregnancies without complications (76%), were term born (89%), and had obtained 1 minute Apgar scores of 9–10 (72%). The percentage of breastfed subjects (for at least 6 months) was 79% (Table 1).

Analysis of the intra-TEM for height and weight amounted to 13 g and 0.39 cm respectively. Inter-TEM had higher values and amounted to 194 g for weight and 0.41 cm for height. The average bias was 0.22 cm for height (underestimated) and 110 g for weight (overestimated). The coefficient of reliability was 0.99 for weight and 0.86 for height.

There were no significant differences from the population reference data in age- corrected height (Table 2), but the total cohort had significantly greater weight (z=0.28, t=2.93, p=0.003), and BMI (z=0.25, t=2.62, p=0.009) z scores. After division of the sample according to age, statistically significant differences were found for body weight in the range of 6–10 years (z=0.31, t=2.50, p=0.013) and BMI in the range of 11–15 years (z=0.42, t=2.15, p=0.032).

Table 2.

The Comparison of the Mean Levels of Children's Height, Weight and Body Mass Index (BMI) with the Population Norm and the Comparison of the Mean Growth Levels Between the Age Ranges

		Body height					Body weight					BMI
Age ranges	n	Mean ^a	SD	df	t	p	Mean ^a	SD	df	t	p	Mean ^a	SD	df	t	p
6–10y	82	0.12	1.17	830	1.00	0.319	0.31	1.45	830	2.50	0.013	0.22	1.22	830	1.85	0.065
11–15y	30	−0.16	1.53	528	−0.85	0.626	0.25	0.87	528	1.35	0.178	0.42	1.52	528	2.15	0.032
16–18y	23	−0.35	1.75	521	−1.56	0.120	0.19	1.84	521	0.83	0.406	0.09	1.35	521	0.43	0.668
Total cohort (6–18 years)	135	0.01	1.30	1132	0.16	0.875	0.28	1.40	1132	2.93	0.003	0.25	1.29	1132	2.62	0.009
ANOVA (for the distinguished ranges)		F=1.26p=0.290					F=1.36p=0.260					F=0.70p=0.500

Mean of z score in the categories.

SD=standard deviation; df=degrees of freedom; t=Student's t test value; ANOVA=analysis of variance.

Statistically significant differences are in bold.

When the different age ranges within the cohort were compared using parametric ANOVA, there were no significant differences in z scores for height (F=1.26, p=0.290), weight (F=1.36, p=0.260), or BMI (F=0.70, p=0.500).

The forward stepwise regression method was used to build the multifactorial models separately using height, weight, and BMI as the dependent variable. The models showed statistically significant effects of the independent variables at the level of p<0.01 (the model for height: F=5.733, R² =0.443, p<0.001; the model for weight: F=3.726, R² =0.265, p=0.021; the model for BMI: F=2.679, R² =0.283, p=0.038).

The final model of height explained 44% of the variable variance (Table 3). There were two variables that had statistically significant independent effects on height: Child birth condition (1 minute Apgar scores) (β=0.366, t=2.824, p=0.008) and the parents' mean height (β=0.239, t=2.340, p=0.025). Variables that did not reach statistical significance were birth body weight and household members per room.

Table 3.

The Multifactorial Model of Height Determination of Boys with Attention-Deficit/Hyperactivity Disorder (ADHD)

Variables	β	β standard error	B	B standard error	t	p
Apgar score (first minute)	0.366	0.130	0.384	0.136	2.824	0.008
Parents' mean height	0.329	0.141	0.059	0.025	2.340	0.025
Birth body weight	0.278	0.140	0.054	0.027	1990	0.055
NHM/RN	−0.161	0.121	−0.196	0.196	−1.268	0.213
Free parameter			−22.58	5.278	−4.236	>0.001
R ²				0.443
F				5.733
p				<0.001
Standard error of estimate				0.995

NHM/RN=number of household members per number of rooms in the household.

Statistically significant differences are in bold.

The weight determination model showed three variables that had an independent effect on weight (Table 4). Of these, only the level of parents' education (β=−0.358, t=−2.202, p=0.035) was statistically significant.

Table 4.

The Multifactorial Model of Weight Determination of Boys with Attention-Deficit/Hyperactivity Disorder (ADHD)

Variables	β	β standard error	B	B standard error	t	p
Level of parents' education	−0.358	0.162	−0.616	0.280	−2.202	0.035
Birth body weight	0.319	0.159	0.001	0.001	2.003	0.054
NHM/RN	−0.217	0.158	−0.318	0.232	−1.367	0.181
Free parameter			0.096	1.155	0.083	0.934
R ²				0.265
F				3.726
p				0.021
Standard error of estimate				1.159

NHM/RN=number of household members per number of rooms in the household.

Statistically significant differences are in bold.

For BMI, only the parents' mean BMI had a statistically significant effect (β=0.430, t=2.453, p=0.019). Three other variables with an effect that did not reach significance but were included in the model were: household members per room, birth body weight, and place of residence (Table 5).

Table 5.

The Multifactorial Model of Body Mass Index (BMI) Determination of Boys with Attention-Deficit/Hyperactivity Disorder (ADHD)

Variables	β	β standard error	B	B standard error	t	p
Parents' mean BMI	0.430	0.175	0.126	0.051	2.453	0.019
NHM/RN	−0.293	0.176	−0.567	0.346	−1.663	0.106
Birth body weight	0.288	0.157	0.001	0.001	1.833	0.075
Place of residence	−0.269	0.163	−0.488	0.295	−1.651	0.108
Free parameter			−0.789	2.059	−0.383	0.704
R ²				0.283
F				2.679
p				0.038
Standard error of estimate				1.175

NHM/RN=number of household members per number of rooms in the household.

Statistically significant differences are in bold.

Discussion

In the present study, we assessed the growth of treatment-naïve boys with ADHD. The study revealed lack of statistically significant deviations in z scores for height of the boys with ADHD in comparison with the norm, and significantly higher values of z scores for body weight and BMI in the total cohort. After categorization of the subjects according to age ranges, statistically significant differences were noted for body weight in the range of 6–10 years and for BMI in the range of 11–15 years. However, when we compared the different age categories with each other, analysis did not show statistically significant differences. It rather indicated no significant role of age in the determination of variance of the results for these variables.

Our study is in accordance with the results of some research that did not show statistically significant deviations from normal in the growth in height of children (Biederman et al. 2003) or adults with ADHD (Hetchman et al. 1984. Kramer et al. 2000). However, greater body sizes have been found in untreated preschool children with ADHD (Swanson et al. 2006). They were taller by 0.45 z scores and heavier by 0.78 z scores in comparison with the growth charts. Our results are consistent with normal growth in height over the entire study age range.

In contrast, the subjects on average had higher body weight and BMI in comparison with the norm, which is in accordance with the baseline data of Zachor et al. (2006), Bereket et al. (2005) and weight in an untreated subsample in the study of Swanson et al. (2007) and Ptáček et al. (2009b,c). Our results would support greater tendency for overweight in children with ADHD in comparison with the general population (Curtin et al. 2005; Hubel et al. 2006; Lam and Yang 2007; Cortese et al. 2008; Hanć and Cieślik 2008a).

The study included assessment of intra-TEM, inter-TEM, the average bias, and the coefficient of reliability of body height and weight measurements. The values for intra-TEM (13 g, 0.39 cm) were substantially lower than the admissible errors in anthropometry (200 g and 1 cm) (Chumlea and Guo 2006). Inter-TEM had higher values but was similarly acceptable. Various sources state different permissible minimum levels of reliability in anthropometric measurements. The minimum value ranges from 0.85 to 0.95 (Ulijaszek and Kerr 1999; Moreno et al. 2003). We noted greater error in the measurement of height. It was not possible to measure all the subjects at the same time of the day, which is recommended due to diurnal variation during standing upright, which may amount to 0.47 cm (±− 0.05 cm) between the measurement taken from 9.00–10.00 o'clock and the measurement taken from 15.00–16.00 (Siklar et al. 2005). This could also have contributed to our measurement error. It should also be mentioned that observers measured boys with ADHD: Hyperactivity contributed to difficulties in positioning the subjects in the standard position, which may have increased the measurement error. This emphasizes the importance of including information on the measurement accuracy.

Children with ADHD often exhibit comorbid disorders, such as depression or anxiety disorders (Wilens et al. 2002) that may require using medications other than stimulants. Side effects of some psychotropic medications include changes in body weight (Klein et al. 2006; Fleischhaker et al. 2008) and can therefore be important confounders of studies of growth of children with ADHD. In the present study we assessed the growth of treatment-naïve boys with ADHD. In contrast to most of the previous studies, we included biological factors as height and BMI of parents; factors related to newborn condition – birth body weight, Apgar score; and factors related to socioeconomic status and life style – parents' education level, number of people living in a given household, place of residence. The influence of these variables on the process of growth in the general population has been described in the literature (compare to, e.g., Tanner 1992; Zhong-Cheng et al. 1998; Vázquez et al. 1998; Dimitriev et al. 2006; Eiben and Mascie-Taylor 2004; Johnston 2006; Lejarraga 2006; Towne et al. 2006; Trebar et al. 2007). In the sample of the examined boys with ADHD, height, body proportion of parents, newborn condition (Apgar score), and the level of parents' education had the strongest influence. These variables might represent unrecorded confounders in other studies.

The higher body weight and BMI observed in children of parents with lower education level and those living in rural areas and in small towns, respectively, might result from less health awareness within these families. However, it should be noted that values of R² in height, weight, and BMI determination models suggest that important variables influencing the process of growth in children with ADHD could have been beyond control in the present research. We did not assess the intensity of the neuropsychological symptoms of ADHD (e.g., impulsivity) or the presence of comorbid disorders (e.g., binge eating). These and other factors might be important (Davis et al. 2009; Davis 2010; Zwaan et al. 2011).

Limitations

The study had a few limitations. In the absence of clinical data regarding pubertal staging, the age criterion was used. In our previous study we adopted the following age categories:<12.5 years – prepubertal; 12.5–15.00 – pubertal; >15 – postpubertal. Recent research on Polish youth indicates an earlier onset of puberty of boys (Gołąb and Burdecki 2007); therefore, in the present study we decided to change the lower limit of puberty (11 years) and maintain the upper limit (15 years), and thus we also extended the age range, when all the most important changes related to puberty “should” occur. The change, however, which we consider justified, causes some difficulties in comparing the results obtained in the present study with the results of our previous studies (Hanć and Cieślik 2008a,b).

The study did not include a control group of children without ADHD. This is important because there may be correlations between ADHD and some of the biological and social indicators. Furthermore, the reference data used in standardization were published in 2001, whereas the measurements of the boys with ADHD were taken from 2004 to 2008. As we did not compare the data of the sample with a time-matched control group of normal children, we cannot exclude that the obtained results for body weight and BMI are a result of a secular trend. However, results obtained in other studies demonstrate greater risk of overweight and obesity in children, youth, and adults with ADHD when compared with the control group (Cortese et al. 2008; Davis et al. 2009; Davis 2010; Zwaan de et al. 2011), which supports our findings.

The majority of the boys in our sample were<11 years of age (n=82, 61%). The subsamples of boys in the age ranges 11–15 and 16–18 years consisted of 30 (22%) and 23 (17%) individuals, respectively. Based on the small numbers of boys in the later developmental stages, it is possible that our results might have failed to detect significant effects in these groups. As the results of Hanć and Cieślik (2008b) suggest a difference between boys with ADHD and the comparison group at the age of the onset of the growth spurt, further studies with greater numbers could provide some important data extending our knowledge on the growth of children with ADHD.

Conclusion

The present study is the first one among investigations concerning the growth of children with ADHD that provides full assessment of the measurement accuracy. The study revealed that the level of height in boys with ADHD not treated pharmacologically was not significantly different than the growth charts. The investigation also showed a tendency for greater weight and BMI in boys with ADHD than in the general population, which might imply a greater risk of overweight and obesity in boys with ADHD. The study also indicates the necessity in further research of controlling statistically for such variables as body size of parents, perinatal factors, and socioeconomic and cultural conditions of upbringing, which might impact on the growth of children with ADHD.

Clinical significance

The results of our research suggest that boys with ADHD may on average have greater body weight and BMI than the norm. This supports the premise that ADHD may be a risk factor for overweight and obesity.

Footnotes

Disclosures

No competing financial interests exist.

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