Growth Dysregulation and ADHD

Abstract

Objective: A small literature suggests that ADHD may be associated with dysregulated growth, but this prior work primarily used clinically referred samples, so it faces difficulties of interpretation. The objective of this study is to sample the general French population for ADHD and evaluate if ADHD is associated with dysregulated growth. Methods: Starting with 18 million telephone numbers, 7,912 were randomly called. Among 4,186 eligible families, 1,012 were recruited. The goal was for the final distribution of the sample to match the demographic background of the French population. A telephone interview was administered to all families to diagnose ADHD and assess other psychopathology and functioning variables. Results: Medication-naïve ADHD was associated with being taller, t(515) = 26.3, p < .001, and heavier, t(518) = 1.8, p = .03, for young children. In contrast, for older children, medication-naïve ADHD participants were shorter and lighter. These results were stronger for weight than height. Conclusion: Although these data do not cast doubt on the well-documented association of stimulant treatment with delays in growth, they provide some support for the idea that, in the absence of medication exposure, ADHD is associated with dysregulated growth.

Keywords

ADHD height weight growth epidemiology

Several studies suggest that ADHD might be associated with dysregulated growth. For example, Steinhausen, Dorr, Kannenberg, and Malin (2000) examined the behavior profile of 311 children and adolescents with short stature and found such children to exceed population norms on all symptoms from the Child Behavior Checklist and Youth Self-Report. Swanson, Ruff, Feldman, Furr, and Allen’s (2005) study of 4,434 children with ADHD ages 6 to 17 presented height z-score data for prepubertal, pubertal, and late pubertal youth. For stimulant-naïve youth, the z scores were 0.32, 0.19, and 0.03, respectively. These data indicated that, compared with population norms (where the average z score is 0.0), children with ADHD are taller than average, with the effect being greatest for the youngest children. Waring and Lapane (2008) studied 62,887 children aged 5 to 17 years in an American population sample. ADHD children not treated with medication had 1.5 times the odds of being overweight compared with the non-ADHD population.

Kaffman, Sher, and Bar-Sinai (1979) suggested that ADHD might itself be associated with dysregulated growth. In their study of stimulant-associated growth deficits among children with minimal brain dysfunction (MBD), they found that untreated MBD children showed wider individual variations in the intensity, rate, and tempo of physical development that could lead to unexpected shifts in growth percentiles with treatment. Spencer et al. (1996) suggested that ADHD’s pathophysiology might cause a delayed tempo of growth, which is temporary and does not lead to a permanent stunting of growth. This idea was challenged by the MTA Cooperative group (2004) based on their exploratory study of naturalistic subgroups. That analysis (using z-score methodology) found that patients with ADHD who received no medication during the 2-year study period grew at a faster than normal rate than patients with ADHD who received medication. However, in the MTA study, the patients with ADHD who received no medication also grew faster than normal controls, again suggesting some inherent differences in growth associated with the disorder itself.

In a longitudinal study, Hanc and Cieslik (2008) studied 53 stimulant-naïve ADHD youth between the ages of 6 and 17. Consistent with the studies reviewed above, the mean height z score for these ADHD youth was significantly greater than the expected average of zero, with the z score across all age groups averaging 0.28. Notably, z scores were higher for prepubertal (0.30) and postpubertal (0.40) children compared with pubertal children, who had normal z scores (0.01). Thus, stimulant-naïve ADHD youth show an atypical pattern of growth spurts that might account for some of the growth delays observed in stimulant treatment studies.

Cortese et al. (2008) reviewed the small literature assessing the prevalence of ADHD among patients attending an obesity clinic. The four available studies reported prevalences of 27.4%, 13.3%, 57.7%, and 38.6%. Although interpretation of these results is difficult because of the lack of nonobese control groups, the prevalences reported are much larger than one would expect from epidemiological studies of ADHD.

Faraone, Biederman, Morley, and Spencer (2008) reviewed 20 psychopharmacologic studies that assessed the height and weight of youth with ADHD prior to stimulant treatment. Prior to treatment, the average z score for weight was 0.47. The 95% confidence interval for this mean ranged from 0.39 to 0.56, which indicates that, prior to treatment, youth with ADHD are significantly heavier than their peers of the same age. The mean height z score prior to treatment was 0.8. Although this indicates that the ADHD youth were somewhat taller than expected, the 95% confidence interval was −0.01 to 0.17, which indicates that the difference with the normative height z score of zero is not significant.

Using an epidemiologic study of children with ADHD in France, the present study sought to test the hypothesis that ADHD is associated with growth dysregulation (Lecendreux, Konofal, & Faraone, 2010). This sample has two advantages lacking in prior studies of ADHD and growth. Because the rate of medication treatment for ADHD is very low in France, this sample allows us to examine growth effects in the absence of medication effects. In addition, because we used population-based ascertainment, our results cannot be due to the types of ascertainment biases that might affect clinically referred samples.

Method

Ascertainment

The sample was selected in December 2008 by the survey company IDDEM, which specializes in population-based epidemiologic telephone surveys. Starting with a base of 18 million telephone numbers, 7,912 were randomly selected. When each phone number was called, we first asked for consent to proceed and then asked questions to determine the following demographic features of the family: administrative region; size of the village, town, or city; marital status of the parents; and occupation of the head of household. Our goal was for the final distribution of our sample to match the distribution of the French population on each of these variables. The intent of this survey was to examine the incidence of ADHD in children aged 6 to 12.

Of these 7,912 telephone numbers, 1,663 were no longer in service. Of the remaining 6,249 households, 2,063 were not eligible because they did not have a child between the ages of 6 and 12 or they belonged to a quota stratum that had already been filled. Among the 4,186 eligible families, 1,012 (24.2%) were successfully recruited into the sample. The remaining households were not successfully recruited for the following reasons: the telephone was not answered (1,064), the telephone line was busy (41), the telephone used caller ID and would not accept anonymous calls (70), the person answering the phone refused to participate (1,342), and the participant decided to stop the interview before it was completed (100). Families were usually called 6 times on different days before being classified as not answering or busy. But, because some quota strata were more difficult to obtain than others, some families were called between 20 and 25 times before being classified as not answering or busy.

Assessment

After a parent in the household gave verbal consent to participate, the interviewer administered a two-part questionnaire. The first part, which was administered to all families, covered the following areas: family living situation, school performance of the child, symptoms of ADHD, conduct disorder (CD), and oppositional defiant disorder (ODD). To reduce the costs of the project, the second part was administered to all families having a child with ADHD and to a randomly selected subset of half the non-ADHD children. The second part included questions about sleep disturbance, eating habits, use of iron as a supplement, and history of treatment for ADHD. During the second part of the interview, parents were asked to give the height and weight of their child. The interviewers were trained by one of the authors (M.L.). The questions about disorders were adapted from a version of the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Aged Children (SADS) (Orvaschel & Puig-Antich, 1987) updated with items addressing all Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria by coauthors M.L. and E.K. All questions were asked about the child’s current functioning. ADHD diagnoses were considered positive only if symptoms had been impairing and had endured for at least 6 months. We defined subthreshold ADHD as having at least one symptom of ADHD without meeting diagnostic criteria for the disorder.

Statistical Analysis

Population data from the Institut National de la Statistique et des Etudes Economiques (INSEE) provided the number of people living in each of the 22 French Administrative Regions and, within each region, each of five population density zones defined as follows: 1 = rural; 2 = nonrural population less than 10,000; 3 = population between 10,000 and 99,999; 4 = population between 100,000 and 1,999,999; and 5 = population greater than 1,999,999. These data were used to create sampling weights for each participant equal to the inverse of the probability of the participant being sampled. These weights were incorporated into all statistical analyses using the survey data estimate routines in STATA 10.0.

Results

Table 1 shows the breakdown by ADHD status and age for participants having data on either height or weight. Among the 536 parents who were administered the second part of our interview, 26 of their children had ADHD, 407 had no ADHD symptoms, and 103 reported some ADHD symptoms. Among the 536 parents, 522 provided their child’s age in months and height in centimeters; 525 provided age and weight in kilograms.

Table 1.

Numbers of Participants Available by Age and ADHD Status

ADHD status	6 years	7 years	8 years	9 years	10 years	11 years	12 years
Not ADHD	22	49	64	76	69	77	47
Subthreshold	14	6	15	19	18	17	11
ADHD	1	2	2	6	2	7	5

We used a regression model to predict weight from ADHD status (yes, subthreshold, no), age in months, and their interaction, and we found a significant main effect for ADHD status, t(518) = 1.8, p = .03; age, t(518) = 21.2, p < .001; and their interaction, t(518) = 1.9, p = .03. Figure 1 plots weight against age in months. The ADHD subgroups are indicated by different shapes. The graph shows that younger children with ADHD tend to be heavier than others whereas older children with ADHD tend to be lighter than others. The effect can be seen more easily in Figure 2, which gives the predicted values from the regression model. The green line for the children with ADHD is flatter indicating that young children with ADHD tend to be heavier than expected and older children with ADHD tend to be lighter than expected.

Figure 1.

Scatterplot of weight and age in months

Figure 2.

Predicted values from the regression of weight on ADHD status, age, and their interaction

To determine whether demographic factors might have confounded these analyses, we first determined whether any of the following demographic factors were associated with the child’s weight: population density zone (defined in the Methods), age of parent interviewed, type of housing (single family vs. collective), education level of parent, profession of head of household, whether the head of household was currently employed, marital status of biological parents, status of adult(s) raising the child (single parent, married couple, nonmarried couple), and number of children in the household. Of these variables, only the profession of the head of household was significantly associated with the child’s weight, t(518) = 1.9, p = .001. However, when we included this variable in the regression model predicting weight from age and ADHD status, the main effect of age, the main effect of ADHD status, and the interaction effect each remained significant (all ps < .05).

In models predicting height, we found a significant main effect of age, t(515) = 26.3, p < .001, and a significant interaction, t(515) = 1.8, p = .04. The main effect of ADHD status was not significant (p > .10). Figure 3 presents the scatterplot of height and age. Figure 4 plots the predicted values from the regression model. The effect for height is not as dramatic as it is for weight, but we can see that for younger children, ADHD is associated with greater height than for non-ADHD children. As we saw for weight, this effect is reversed for older children for whom ADHD is associated with lower heights than non-ADHD children. For both height and weight, the subthreshold children have intermediate results. Two of the potentially confounding demographic variables were associated with the child’s height: profession of the head of household, t(515) = 3.6, p < .001, and type of housing, t(515) = 2.7, p = .007. When we included these variables in the regression model predicting weight from age and ADHD status, the previously significant interaction of ADHD status and age became only marginally significant, t(518) = 1.7, p = .06.

Figure 3.

Scatterplot of height and age in months

Figure 4.

Predicted values from the regression of height on ADHD status, age, and their interaction

Discussion

Our results suggest that there is some merit to the idea that ADHD is associated with a pattern of dysregulated growth. In our data, medication-naïve ADHD was associated with being taller and heavier for young children; in contrast, for older children, medication-naïve ADHD was associated with being shorter and lighter. These results were stronger for weight than height. The effects on weight could not be accounted for by confounding factors. In contrast, the effects on height became marginally significant (p = .06), underscoring the relative weakness of the height effect compared with the weight effect.

Our results are partially consistent with Swanson et al.’s (2005) study of 4,434 clinically referred children with ADHD ages 6 to 17, which found younger stimulant-naïve youth with ADHD to be heavier than expected from population norms. Although they found a decrease in this effect among older children, in contrast to our results, that study did not find lower than expected growth parameters among older stimulant-naïve ADHD youth. Hanc and Cieslik’s (2008) study of 53 stimulant-naïve Polish youth with ADHD between the ages of 6 and 17 found similar results. As was found by Swanson et al., prepubertal, stimulant-naïve youth with ADHD were heavier and taller than expected and pubertal, stimulant-naïve youth with ADHD had normal growth parameters.

Our study agrees that younger, medication-naïve youth with ADHD have greater than expected growth parameters, but we also found that older youth with ADHD had lower than expected growth parameters. This difference may be due to Swanson et al.’s (2005) and Hanc and Cieslik’s (2008) use of clinically referred samples. It is possible that, other things being equal, youth with ADHD who are taller and heavier are more disruptive and thus more likely to be clinically referred than smaller youth with ADHD. In this regard, it is notable that although most studies of clinically referred samples do not provide data stratified by age, as a group, they suggest that youth with ADHD are taller and heavier than normal (Faraone et al., 2008).

These findings have implications for how one interprets the literature about stimulant-associated growth delays in stimulant-treated youth with ADHD. Faraone et al. (2008) presented a quantitative review of studies assessing the effects of stimulants on growth. Studies providing longitudinal data show that stimulant medication leads to delays in expected growth in both height and weight. Although patients continue to grow, they grow less than expected for their age. This effect is greatest for taller and heavier children and is greater for children compared with adolescents.

There are three reasons why the existence of ADHD-associated growth dysregulation cannot completely account for the growth delays observed in stimulant-treated ADHD youth: (a) in youth with ADHD, stimulant-associated growth effects are dose dependent (Faraone et al., 2008); (b) they are also greater for stimulant-naïve ADHD patients (Faraone et al., 2008); and (c) stimulant-associated growth deficits have been observed in non-ADHD childhood cancer survivors treated with methylphenidate to address cognitive sequelae (Jasper et al., 2009). These three effects are not predicted by our findings of growth dysregulation.

However, the existence of ADHD-associated growth dysregulation may exaggerate the degree of stimulant-associated growth delays. This would occur for two reasons. First, because clinically referred youth with ADHD are typically taller and heavier than average, they will regress toward the mean over time, leading to decreases in expected growth. Second, between prepuberty and puberty, our data and those reviewed above find decreases in the degree to which stimulant-naïve youth with ADHD are larger than average. This would tend to exaggerate growth delays observed in longitudinal studies of stimulant use.

Our work should be viewed in the context of several methodological limitations. Because this was a population study, only a small subgroup of participants met criteria for ADHD and subsamples were very small when stratifying by age (Table 1). Because this is a cross-sectional study, we cannot be certain that the pattern of growth differences seen between age groups would also be seen if we studied children over time. Longitudinal studies are needed for this purpose. Another limitation is our use of phone interviews with a parent rather than using an in-person psychiatric interview. Ideally, we would have measured the children’s height and weight directly, but this was not possible because of cost considerations. It is likely that parent reports are not 100% accurate, but it is difficult to conceive a pattern of biased reporting that would explain our results. However, the work of Strauss (1999) suggested that self-reports of growth parameters are valid. They found that the correlation between self-reported weight and actual weight ranged between 0.87 and 0.94, depending on gender or race. The correlation between self-reported height and actual height ranged from 0.82 to 0.91.

There is a large literature suggesting that telephone interviews are valid for assessments of psychopathology. Biederman et al. (1992) found that telephone interviews had high reliability for diagnosing ADHD (κ = 0.93, p < .0001), high sensitivity (95%), and high specificity (98%) when in-person interviews with the mothers were used as the gold standard diagnosis. Telephone interviews used a population survey of 1,000 adults to estimate the prevalence of ADHD (Faraone & Biederman, 2005) and replicated work using in-person interviews (Kessler et al., 2006), and telephone interviews assessing functional impairments associated with ADHD (Biederman et al., 2006) replicated our prior work using in-person interviews (Barkley, Murphy, & Fischer, 2008; Biederman et al., 1993). In the first report from this sample, we found that telephone interview diagnoses with ADHD replicated many of the known clinical features of ADHD such as association with comorbid psychiatric disorders, increased rates of school failure, and familial transmission (Lecendreux et al., 2010). Another limitation of our work is that we used only one informant (a parent) to collect information about the child. Others have shown that the estimated prevalence of ADHD is sensitive to the choice of informants and the number of informants (Brown et al., 2001; Shekim et al., 1985).

Despite these limitations, our work is consistent with a small body of literature suggesting that ADHD is associated with growth dysregulation. The nature of this dysregulation is such that younger children with ADHD are heavier and taller than expected, and older children with ADHD are lighter and shorter than expected. Although these data do not cast doubt on the well-documented association of stimulant treatment with delays in growth, it is possible that the magnitude of that association has been overestimated.

Footnotes

Acknowledgements

The authors express their appreciation to the survey company IDDEM () for their work in implementing the telephone interviews and to Professor Pierre Philip, MD, for introducing them to IDDEM.

Authors’ Note

In the past year, Dr. Stephen Faraone has received consulting fees and has been on advisory boards for Eli Lilly and Shire and has received research support from Eli Lilly, Pfizer, Shire, and the National Institutes of Health. In previous years, Dr. Faraone has received consulting fees or has been on advisory boards or has been a speaker for the following sources: Shire, McNeil, Janssen, Novartis, Pfizer, and Eli Lilly. In previous years, he has received research support from Eli Lilly, Shire, Pfizer, and the National Institutes of Health. In the past year, Dr. Michel Lecendreux has received consulting fees and has been on advisory boards for UCB and Shire and has received research support from Shire. In previous years, Dr. Lecendreux has received consulting fees or has been on advisory boards or has been a speaker for Cephalon, UCB, Shire, and Eli Lilly. In the past year, Dr. Eric Konofal has received consulting fees and has been on advisory boards for Pierre Fabre, Shire, UCB, Janssen-Cilag, and Pharmacosmos. In previous years, Dr. Konofal has received consulting fees or has been on advisory boards or has been a speaker for UCB, Janssen-Cilag, GSK, and Vifor.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article:

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: This work was supported by Shire Development, Inc.

Bios

Stephen V. Faraone, PhD, is a professor in the departments of Psychiatry and Neuroscience & Physiology at SUNY Upstate Medical University and Director of Medical Genetics Research for the University. He is also Senior Scientific Advisor to the Research Program Pediatric Psycho-pharmacology at the Massachusetts General Hospital and a lecturer at Harvard Medical School. Dr. Faraone is principal investigator on several National Institute of Health funded grants studying the nature and causes of mental disorders in childhood. He is one of the world’s leading authorities on the genetics of psychiatric disorders and has also made substantial contributions to research in psychopharmacology and research methodology. An author on over 550 journal articles, editorials, chapters and books, he was the eighth highest producer of High Impact Papers in Psychiatry from 1990 to 1999 as determined by the Institute for Scientific Information (ISI, Science, 2000, Vol 288, pg 959). In 2005, ISI determined him to be the second highest cited author in the area of Attention Deficit Hyperactivity Disorder (http://www.esi-topics.com/add/interviews/StephenFaraone.html) and in 2007 he was the third most highly cited researcher in psychiatry () for the preceding decade. Dr. Faraone is Co-Editor for the journal Neuropsychiatric Genetics and for the Journal of ADHD and Related Disorders. He is also Deputy Editor for the Journal of the American Academy of Child and Adolescent Psychiatry, Assistant Editor for the Journal of Attention Disorders and Associate Editor for the Journal of Child and Adolescent Psychopharmacology and Behavioral and Brain Functions. He sits on the Editorial Boards for Biological Psychiatry, and the Journal of Child Psychology & Psychiatry. In 2002, Dr. Faraone was inducted into the CHADD Hall of Fame in recognition of outstanding achievement in medicine and education research on attention disorders and in 2004 and 2008 he was elected to the Vice Presidency of the International Society of Psychiatric Genetics. In 2008, he received the SUNY Upstate President’s Award for Excellence and Leadership in Research.

Michel Lecendreux, MD is a child psychiatrist involved in pediatric sleep research and clinic. “Praticien Hospitalier” and Director of Sleep laboratory at the “Centre Pédiatrique des Pathologies du Sommeil” at Hospital Robert Debré in Paris, his main fields of interests are sleep, alertness, narcolepsy and Attention Deficit Hyperactivity Disorder. He recently became a director of the French Reference Centre for Pediatric Narcolepsy and rare Hypersomnia in Paris. Dr Lecendreux is an author on journal articles, editorials, chapters and books in the area of ADHD and sleep and an Editorial Board Member for the Journal of Attention Disorders. Together with his colleagues he reported on the role of vigilance impairment in ADHD children and insisted on the role of iron deficiency in the pathophysiology of ADHD. Dr Lecendreux is also involved in numerous activities including teaching medicine and education research on attention disorders at the Faculty of Medicine of Paris.

Eric Konofal, MD, PhD, is a medical consultant for the Pediatric Sleep Disorder Center as well as the child and adolescent psychiatry department at the Hospital Robert Debré. He is also a consultant and clinical investigator for the sleep Unit at the Pitié-Salpêtrière University Hospital, Paris. Dr. Konofal is a nationally and internationally recognized sleep disorder researcher, scientist, and medical investigator. He has also been a clinical investigator of neurology at Clinical Investigation Center (CIC, INSERM) of the Pitié-Salpêtrière University Hospital under the supervision of Professor Yves Agid. Dr. Konofal specializes in the study of ADHD, RLS, and alertness disorders, consulting at the aforementioned sleep disorder centers. His research interests are focused on brain- and iron-dopamine interactions in subjects with neurological sleep disorders (as RLS, PLMS), and ADHD. He has still initiated previous studies based on iron and its interest in pathophysiology of ADHD and has explored the relationship between brain and iron. He has launched a clinical trial on efficiency of mazindol in children with ADHD (clinicaltrials.gov identifier: NCT00508677). Dr. Konofal is also gold member of scientific committee of ADHD french association of patients (HYPERLINK” http://www.tdah-France.fr” ), member of Inter-national Restless Legs Syndrome Study Group committee, member of ADHD European Guidelines scientific, expert for the french drugs administration (AFSSAPS) and consultant for EMEA (PIP).

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