Malnutrition and Obesity in Children With ADHD

Abstract

Objective: Low threshold of dissatisfaction and frustration in child and adolescent patients with ADHD lead to changes in dietary pattern with contribution to the development of obesity and other nutritional disorders. Methods: A total of 362 patients with the diagnosis of ADHD and 390 children without any chronic disease were included to the study. We evaluated the anthropometric measurements for all cases. Results: The overweight/obese cases according to weight for height (WFH) and body mass index scores were more likely in ADHD cases than control group. Malnourished cases according to WFH were more common in ADHD cases. Patients with weight standard deviation scores between <−2 and >2 were significantly higher in ADHD cases than the control group patients. Conclusion: Frequency of malnutrition and overweight/obese patients was found higher in ADHD cases compared with control group, and this situation suggested that ADHD is a risk factor for the development of eating disorders.

Keywords

malnutrition obesity attention deficit hyperactivity children

Introduction

ADHD, one of the most frequent neurobehavioral disorders in childhood characterized by hyperactivity, impulsivity, and attention deficit symptoms, affects 3% to 7% of schoolchildren (American Psychiatric Association [APA], 1994; Cantwell, 1996). As defined by the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000) criteria, there are three subtypes of ADHD: combined, inattentive, and hyperactive-impulsive. Children with this disorder experience problems with self-esteem, educational difficulties, significantly impaired family and peer relationships, and an overall decrease in quality of life (Hoza et al., 2004; Kawabata, Tseng, & Gau, 2012).

Childhood obesity is considered as a signal of a major public health problem. There is some evidence that there is a predisposition to obesity in children with chronic neurological problems (Chen, Kim, Houtrow, & Newacheck, 2010), especially in those with ADHD (Agranat-Meged et al., 2005; Chen et al., 2010; Curtin, Bandini, Perrin, Tybor, & Must, 2005; Waring & Lapane 2008). Some potential psychopathological and pathophysiological mechanisms linking ADHD to obesity have been hypothesized. While ADHD might lead to obesity through impulsive and disorganized eating behaviors, on the contrary, obesity might lead to ADHD symptoms through sleep-disordered breathing, which has been associated with a pattern of inattention and hyperactivity/impulsivity in childhood (Cortese et al., 2008). However, some studies found no relationship between ADHD and obesity (Biederman et al., 2003; Curtin et al., 2005; Dubnov-Raz, Perry, & Berger, 2011).

In most of the studies, relationship of obesity and ADHD was investigated by using only body mass index (BMI) values. In this study, we presented nutritional status of children and adolescents with ADHD using anthropometric measurements in a more detailed manner and aimed to investigate probable relationship between ADHD and obesity.

Material and Methods

A total of 362 children between ages of 5 and 15 years followed at the child psychiatry clinic with the diagnosis of ADHD according to the criteria of DSM-IV-TR were included in the study. Diagnosis was made by two independent child psychiatrists, who used the Turkish version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL; Gökler et al., 2004; Kaufman et al., 1997). Age- and sex-matched 390 children, who applied to general pediatrics outpatient clinic for mild upper respiratory infection, were included as control group. Demographic characteristics, diet and medication history, clinical signs, weight for height (WFH), BMI, weight standard deviation scores (SDS), and triceps and subscapular skinfold thickness (SFT) values of all cases were recorded. ADHD and control participants were classified as wasted (WFH ≤ 89), well nourished (normal; 90 < WFH < 110), and overweight/obese (WFH ≥ 110) according to their WFH values (Waterlow, 1972). Interpretation of BMI was made according to age and sex, using national percentile curves (Kondolot et al., 2011; Ozturk et al., 2008). Children, whose BMI is greater than or equal to the 85th percentile (≥85%) and is at or above the 95th percentile (≥95%), were considered as overweight and obese, respectively. Children with a BMI below the fifth percentile were considered as underweight.

The cases were divided into three groups according to their weight SDS: SDS <−2 (wasting), 2 ≤ SDS ≤ −2 (normal weight or well nourished), and SDS > 2 (overweight/obese). Harpenden caliper was used for the measurement of SFT. Reference percentile curves formed by Ozturk et al. (2009) and Tanner and Whitehouse (1975) were used for children older and younger than 6 years, respectively. Percentile values lower than 5 were considered as wasting, between 5 and 95 as normal weight, and higher than 95 as overweight/obese.

SPSS for Windows Version 16.0 statistical software package was used for the statistical evaluation of data. The categorical (qualitative) data were presented with the number and percentage. Fisher’s exact chi-square and Pearson’s chi-square tests were used for the comparison of the groups for categorical (qualitative) variables. The p value <.05 was considered as statistically significant.

Ethical committee of the medical faculty approved the study, which has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. In addition, all parents gave informed consent prior to their inclusion in the study.

Results

Hyperactivity (82.9%), distractibility (82.9%), course failure (76.5%), and seizures (8.3%) were reported as symptoms of ADHD cases. Of all patients, 58 (16%) were on stimulant medication (methylphenidate).

Frequency of malnutrition and obesity according to WFH evaluation was significantly higher in ADHD group compared with control group (p < .0001; Table 1). Severity of malnutrition was also different between ADHD and control groups (p = .004; Table 1).

Table 1.

Patient Characteristics of ADHD and Control Group.

	ADHD (n = 362)	Control (n = 390)	p
	M ± SD	M ± SD	p
Age	9.10 ± 2.51	9.30 ± 3.25	.344
Gender
Male	310	315	.091
Female	52	75
Weight SDS	0.012 ± 1.11	−0.23 ± 0.78	.059
Height SDS	0.017 ± 0.91	−0.34 ± 0.91	.057

Note. SDS = standard deviation scores.

When anthropometric parameters were evaluated according to weight SDS, and triceps and subscapular SFTs, prevalence of wasting and overweight/obesity was also higher in patients with ADHD compared with controls (p = .001, p = .0001, and p = .0001, respectively; Table 2). The prevalence of overweight and obesity in children with ADHD according to BMI values was 9.4% and 7.1%, respectively. Total prevalence of overweight/obesity in ADHD group was 16.5%. On the other hand, prevalence of overweight and obesity in control group was 1.5% and 0.2%, respectively. The total prevalence in control group was 1.7% (Table 2). Table 3 shows the nutritional status of children according to WFH calculation.

Table 2.

Comparison of Some Anthropometric Measurements Between ADHD and Control Groups.

	ADHD	Control	χ²	p
	n (%)	n (%)	χ²	p
BMI
BMI ≤ 5 percentile (underweight)	61 (16.8)	62 (15.8)	30.83	.0001
85 ≤ BMI < 95 percentile (overweight)	34 (9.4)	6 (1.5)
BMI ≥ 95 percentile (obese)	26 (7.1)	1 (0.2)
Weight SDS
SDS < −2 (wasting)	8 (2.2)	3 (0.8)	15.16	.001
−2 ≤ SDS ≤ 2 (normal weight)	336 (92.8)	384 (98.5)
SDS > 2 (overweight/obese)	18 (5.0)	3 (0.8)
Height SDS
SDS < −2 (stunting)	19 (5.2)	14 (3.6)	22.72	.0001
−2 ≤ SDS ≤ 2 (normal height)	319 (88)	374 (95.9)
SDS > 2 (height)	24 (6.6)	2 (0.5)
Triceps SFT percentile
Group 1 (wasting)	53 (14.6)	11 (2.8)	52.35	.0001
Group 2 (normal weight)	279 (77.1)	371 (95.1)
Group 3 (overweight/obese)	30 (8.3)	8 (2.1)
Subscapular SFT percentile
Group 1 (wasting)	57 (15.7)	9 (2.3)	48.35	.0001
Group 2 (normal weight)	298 (82.3)	380 (97.4)
Group 3 (overweight/obese)	7 (1.9)	1 (0.3)

Note. BMI = body mass index; SDS = standard deviation scores; SFT = skinfold thickness.

Table 3.

Distribution of Malnourished and Obese Patients With Respect to WFH Scores.

	ADHD group		Control group		χ²	p
	n	%	n	%	χ²	p
WFH ≥ 110 (overweight/obese)	90	24.8	74	18.9	18.94	.0001
90 < WFH <110 (normal weight)	170	47	244	62.5
WFH ≤ 89 (wasting)	102	28.1	72	18.4
Severe (WFH < 70)	11	3.3	1	0.2
Moderate (70 ≤ WFH ≤ 79)	28	7.7	11	2.8	10.97	.004
Mild (80 ≤ WFH ≤ 89)	63	17.4	60	15.4	10.97	.004

Note. WFH = weight for height.

Children with ADHD were evaluated according to their medication usage. Drug usage rate was not significantly different in malnourished and overweight/obese children when compared with normal weight participants (p = .14 and p = .77). Mean weight and height SDS, BMI, and WFH values were not different between those on or off medication (p = .612, p = .610, p = .338, and p = .614, respectively). Prevalence of malnutrition and obesity was not different when evaluated according to weight and height SDS scores, triceps and subscapular SFT, and WFH classification, either (p = .370, p = .298, p = .101, p = .505, and p = .196, respectively).

Hyperactivity frequency was not different in overweight/obese and malnourished patients of ADHD group when compared with normal weight patients (p = .485 and p = .183, respectively).

Discussion

One of the most important ADHD associated comorbidity is obesity, prevalence of which is increasing gradually (Altfas, 2002; Chen et al., 2010; Waring & Lapane, 2008). It was reported that the prevalence of overweight and obesity was between 19.6% to 29% and 7.2% to 18.9% in children with ADHD, respectively (Chen et al., 2010). There are different opinions regarding the mechanism of excess weight gain in ADHD patients. ADHD could lead to impairment of balance between energy intake and expenditure by influencing physical activity, metabolism, and eating habits (Dubnov-Raz et al., 2011). Impulsivity and poor behavioral regulation due to illness contribute to changes in dietary pattern and the development of obesity (Agranat-Meged et al., 2005; Waring & Lapane, 2008). It is harder for high-impulsive people resisting to food intake, which might result in higher energy intake and obesity (Guerrieri, Nederkoorn, Schrooten, Martijn, & Jansen, 2009). Interestingly, impulsive children ingested significantly more calories than their less impulsive counterparts when faced with foods that varied in color, form, taste, and texture, but not in the monotonous food environment (Guerrieri, Nederkoorn, & Jansen, 2008). It has also been reported that depression and anxiety was nine times more frequent in patients with ADHD than the control group (Waring & Lapane, 2008) and depression contributes to the development of obesity (Biederman, Spencer, Monuteaux, & Faraone, 2010; Chen et al., 2010; Waring & Lapane, 2008). On the other hand, it was specified that ADHD and excess weight share a common biological mechanism (dopaminergic reward system) and overweight/obesity could provoke development of ADHD by neural and other mechanisms (Dubnov-Raz et al., 2011).

According to our results, frequency of overweight/obesity according to WFH criteria was higher in ADHD group compared with control group (24.8% vs. 18.9%). When we did the same evaluation according to BMI, which has been often used in the literature, frequency was still higher in ADHD group: overweight (9.4% vs. 1.5%) and obesity (7.1% vs. 0.2%). Regional population studies reported the prevalence of overweight in children aged 6 to 11 years according to BMI as 13.2% and prevalence of obesity as 1.6% (Pirinçci, Durmuş, Gündoğdu, & Açik, 2010). Subscapular and triceps SFT evaluation, which has not been used for assessment of nutritional status in patients with ADHD in earlier studies, revealed similar findings, as well. In a large series of children between the ages of 10 and 17 years, Chen et al. (2010) found the prevalence of obesity as 12.2% in healthy cases, whereas it was 23.4% in autism and 18.9% in ADHD patients, and they concluded that the prevalence of obesity in children with chronic neurological problems is higher. They reported that this situation might be associated with the environmental and socioeconomic characteristics, dietary patterns, physical activity level, chronic problem itself, increased caloric intake or decreased physical activity, depression/anxiety, and behavioral problems (Chen et al., 2010). Unlike that study, Curtin et al. (2005) indicated that overweight/obesity prevalence in children aged between 2 and 18 years was similar to the normal population but only patients with ADHD between 2 and 5 years had a greater risk of obesity. The study of Agranat-Meged et al. (2005) revealed that in 57.7% of hospitalized children because of obesity (BMI > 85), diagnosis of ADHD was established, an important example that shows the relationship between obesity and ADHD. Waring & Lapane (2008) reported the risk of overweight as 15.6% and obesity as 21% independent of gender, age, ethnicity, depression, and anxiety in patients with ADHD, who were not on medication.

However, some studies reported that ADHD does not have any effect on overweight or obesity (Biederman et al., 2003; Curtin et al., 2005; Dubnov-Raz et al., 2011). Dubnov-Raz et al. (2011) reported that the prevalence of overweight and obesity was lower in 6- to 16-year-old 275 ADHD cases without neurological comorbidity after a 17-month follow-up when compared with control group, and the national and regional prevalence for the same age was not different. Likewise, Biederman et al. (2010) followed children with ADHD and control group for 10 years and did not detect any difference between two groups in terms of growth parameters.

The greater frequency of malnutrition in patients with ADHD is another important finding of our study. Drug usage rate was not significantly different in malnourished and overweight/obese children when compared with normal weight participants, and malnutrition and obesity were not different between those groups, either. It might be expected that malnutrition in patients with ADHD is associated with excessive mobility and thus increased energy requirements; however, rate of mobility was not different between malnourished and normal weighted patients in our ADHD group. This finding suggested that the nutritional deficit in ADHD might be due to other behavioral problems such as irregular eating habits. It was reported that the prevalence of underweight was 6.7% in patients with ADHD and risk of underweight was 1.6 times higher, especially in cases on medication (Waring & Lapane, 2008). This result was attributed to stimulants, which decrease appetite, impulsivity, and behavioral regulation (Waring & Lapane, 2008). It was reported that the suppression of appetite due to methylphenidate usage, a commonly used drug to treat ADHD, caused weight loss (Curtin et al., 2005; Dubnov-Raz et al., 2011; Ptacek, Kuzelova, Paclt, Zukov, & Fischer, 2009; Waring & Lapane, 2008). The families stated that medication suppressed appetite in children who ate with extreme hunger before. Methylphenidate can reduce resting metabolic rate and decrease spontaneous physical activity, which sounds like it would cause weight gain. However, long-term treatment may have a negative impact on growth (Dubnov-Raz et al., 2011). There are conflicting views in the literature on this issue. In some studies, stimulant treatment was shown to have no effect on overweight and obesity in ADHD, and it has been advocated that there is no change in terms of growth parameters in female and male cases taking treatment (Biederman et al., 2010; Dubnov-Raz et al., 2011). In some other studies, obesity was found more than 1.5 times higher in cases that were not on stimulant treatment (Waring & Lapane, 2008).

In conclusion, higher frequency of malnutrition and overweight/obesity in patients with ADHD compared with control group suggests that ADHD is a risk factor for the development of eating disorders due to behavioral problems such as irregular eating habits. Feeding problems should always be kept in mind when monitoring children with ADHD and all children should be evaluated in terms of overweight and underweight/weight loss in clinical follow-up and necessary support and guidance should be provided.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Serdal Güngör is a child neurologist. His interests include ADHD, epilepsy, and cerebral palsy.

Özgü Suna Celiloğlu is a pediatrician. She works in public hospital.

Sabiha Güngör Raif is a child neurologist. She works in public hospital. Her area of interest is epilepsy.

Özlem Özel Özcan is a child psychiatrist. Her interests include ADHD and autism.

Mukadder Ayşe Selimoğlu is a pediatric gastroenterologist. Her interests include celiac disease and nutrition.

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