Overweight in Boys With ADHD Is Related to Candidate Genes and Not to Deficits in Cognitive Functions

Abstract

Objective: The aim of the study was to assess the relationship of overweight, the polymorphisms of selected candidate genes, and deficits in the executive functions among children with ADHD. Method: We examined 109 boys with ADHD aged between 7 and 17 years. The study indicated variants of 14 polymorphisms in eight candidate genes. We applied seven neuropsychological tests to evaluate the executive functions. Overweight was diagnosed on the basis of the guidelines of the International Obesity Task Force. Results: Analyses revealed significant association between DRD4 rs1800955, SNAP25 rs363039 and rs363043, 5HTR2A rs17288723, and overweight in boys with ADHD. There were no significant differences in the level of neuropsychological test results between patients with overweight and without overweight. Conclusion: Overweight in boys with ADHD is associated with polymorphisms in three candidate genes: DRD4, SNAP25, and 5HTR2A, but not through conditioning deficits in cognitive functions.

Keywords

ADHD obesity overweight executive functions candidate genes

Introduction

ADHD has been intensively investigated as one of the obesity risk factors. Studies in recent years have shown that ADHD is associated with a higher risk of overweight and obesity in school-aged children and adolescents (Hanć, Cieślik, Wolańczyk, & Gajdzik, 2012; Hanć, Słopień, Wolańczyk, Dmitrzak-Węglarz, et al., 2015; Hanć, Słopień, Wolańczyk, Szwed, et al., 2015; Khalife et al., 2014; Racicka, Hanć, Giertuga, Bryńska, & Wolańczyk, 2015; R. Yang, Mao, Zhang, Li, & Zhao, 2013). Current meta-analysis, including 42 studies, has confirmed a significant association of ADHD with obesity in both children and adults (Cortese et al., 2015). However, it is not clear what factors underlie the ADHD–obesity relationship.

In recent years, there have been a few attempts to explain the genetic background of comorbidity of these disorders (Agranat-Meged et al., 2008; Albayrak et al., 2013; Gray et al., 2006; Kernie, Liebl, & Parada, 2000; Lynos et al., 1999). Moreover, the co-occurrence of ADHD and obesity can also be associated with deficits in executive functions. Previous studies indicate that a deficit in executive functions occurs both in obese children and adolescents (Reinert, Po’e, & Barkin, 2013), as well as in patients with ADHD (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Only a few studies have tested deficits in executive functions and self-regulation as a possible cause of increased risk of obesity in children with ADHD, and their results are contradictory (Choudhry et al., 2013; Graziano et al., 2011). Moreover, because the deficits in the executive functions can be shared by ADHD and the obesity phenotype, developing on the same genetic background, it seems reasonable to carry out studies analyzing the total contribution of candidate genes for ADHD and deficit in executive functions as determinants of the risk of obesity in patients with ADHD. So far, however, such studies have not been conducted.

The aim of the present study was to assess the relationship of overweight among children with ADHD with selected polymorphisms in candidate genes for ADHD, which may cause a tendency to excessive weight gain through a neuropsychological mechanism, connected with deficits in executive functions. We assumed that we will demonstrate both the relationship between the polymorphisms and deficits of executive functions with the occurrence of overweight among boys with ADHD, and that the deficits of executive functions underlying obesity in this group will be genetically conditioned.

Materials and Method

Ethical Statements

The study used data collected during a project carried out in the years from 2007 to 2010 that aimed at evaluating the relationship of selected genotypes and alleles of candidate genes with ADHD symptoms and cognitive functions of patients (Słopień, 2011). The project was approved by the Bioethics Committee at the Medical University in Poznan and Warsaw, and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments; it was also consistent with the national law. For the purpose of the project, we have collected the data on selected candidate genes polymorphisms, performed neuropsychological tests, and collected clinical, sociodemographic information and the data on the development and health of children. The participants and their legal guardians were fully informed about the research procedures, and legal guardians gave a written consent to participate in the study.

ADHD Diagnosis

The study group comprised of unrelated patients aged 7 to 17 years with a diagnosis of ADHD. Parents and their children were informed about the possibility of withdrawing from the study at any time. Patients were recruited in the Outpatient Clinic of Psychiatry and Department of Child and Adolescent Psychiatry at the Poznan University of Medical Science, and in the Department of Child Psychiatry at the Medical University of Warsaw.

Patients were diagnosed by two independent psychiatrists through diagnostic interviews with a parent directed toward the ADHD and comorbid disorders (Wolańczyk & Kołakowski, 2005) based on Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association [APA], 2000), and with the use of the ADHD Rating Scale–IV (DuPaul, Power, Anastopoulos, & Reid, 1998) and Conners’ Rating Scale (parents and teachers version; Conners, 1973). The exclusion criteria were organic central nervous system (CNS) dysfunctions (e.g., epilepsy, serious trauma, tumors, and infections of the CNS), endocrine disease (e.g., hypothyroidism, Cushing’s syndrome, growth hormone deficiency, hypogonadism, pseudohypoparathyroidism), genetic syndromes and co-occurring schizophrenia, and bipolar disorder. Drugs used to treat ADHD and other disorders that may affect the CNS (e.g., antihistamines) were withdrawn for 72 hr before performing the neuropsychological tests.

Genetic Studies

ADHD is the most common behavioral and neurodevelopment disorder; thus, neurodevelopmental genetic factors (brain-derived neurotrophic factor [BDNF] and others) are potentially significant contributors in ADHD etiology (Bilgiç, Toker, Işık, & Kılıç, 2016). Biochemical and pharmacological studies indicate a relationship of ADHD with impaired dopaminergic, serotonergic, and noradrenergic systems (Briars & Todd, 2016). According to this knowledge, in the study, we examined genes that were chosen based on a literature review for association studies of genes that could have an impact on ADHD development. Among the selected 14 polymorphisms of eight genes were polymorphisms previously analyzed for the potential relationship with ADHD and the new ones selected due to their functioning in the neural system or a relationship with neurodevelopmental disorders. We examined the genes for dopamine receptor DRD2 (polymorphism rs1799732 associated with schizophrenia, Cordeiro, Siqueira-Roberto, Zung, & Vallada, 2009), DRD3 (rs6280 associated with ADHD, Ettinger, Joober, De Guzman, & O’Driscoll, 2006), and DRD4 (rs1800955 related to ADHD, J. W. Yang et al., 2008; rs1800443 related to impulse control, Zainal Abidin et al., 2015; 48 variable number of tandem repeats [VNTR] related to ADHD, Ettinger et al., 2006), the dopamine transporter DAT (three polymorphisms associated previously with ADHD: rs27072, Gizer, Ficks, & Waldman, 2009; rs463379, Friedel et al., 2007; VNTR 3′-UTR, Ettinger et al., 2006), catechol-O-methyltransferase COMT (rs4680 related to ADHD, Ettinger et al., 2006), presynaptic protein SNAP25 (rs363039 related to ADHD, Söderqvist et al., 2010; rs363043 and rs363050 associated with autism, Braida et al., 2015), serotonin receptor 5HTR2A (rs17288723 related to depression and effects of antidepressant treatment, Horstmann et al., 2010), and BDNF (rs6265 related to ADHD, Gizer et al., 2009).

DNA isolation from peripheral blood, as well as an analysis of the gene DRD4 polymorphism rs1800955, and DAT and DRD4 genes VNTR polymorphism, were performed in the Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences. Studies of other Single Nucleotide Polymorphisms (SNP) were performed at the Department of Genomics at the University of Bonn. The salting-out method was used for the isolation of DNA from peripheral blood (Miller, Dykes, & Polesky, 1988). DRD4 gene polymorphism rs1800955 was analyzed by Polymerase Chain Reaction - Restriction Fragment Length Polymorphism (PCR - RFLP) through amplification of the DRD4 gene promoter region using the procedures and primers described by Jönsson et al. (2001). DAT gene VNTR polymorphism was analyzed with the PCR-VNTR through amplification of the 3′UTR region of the gene using the primers described by Vandenbergh et al. (1992). The examined polymorphism is characterized by a VNTR in the 3′-untranslated region (3′-UTR) of a gene. A single motif has a size of 40 bp, and may be repeated by 3 to 13 times. DRD4 gene VNTR polymorphism was analyzed with the same method through amplification of the DRD4 gene region of exon 3 using primers described by Mill et al. (2002). A single motif has a size of 48 bp. Alleles described so far had from 2 to 11 repeats of this motif. The SNP determination was performed by the IPLEX Gold using a system of Sequenom. The applied methodology allowed for the use of robotic systems and inspection of the signal quality (positive control—1.5%, negative—1.5%, and duplicate samples—1.5%). The analysis with the use of a Sequenom system is based on the multiplex PCR and mass spectrometry (Ross, Hall, Smirnov, & Ha, 1998).

Neuropsychological Examination

The level of executive functions was evaluated based on the results of seven neuropsychological tests. The tests were performed in the following order: computerized version (Borkowska, 2008) of Continuous Performance Test (CPT; Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956), The Stroop Color-Word Interference Test (SC-WIT; Stroop, 1935), The Trail Making Test A and B (TMT; Reitan, 1985), The Matching Familiar Figures Test (MFFT; Kagan, Rosman, Day, Albert, & Phillips, 1964), Verbal Fluency Test (VFT; Lezak, 1995), and The Rey-Osterrieth Complex Figure (ROCF; Osterrieth, 1944). Wisconsin Card Sorting Test (WCST; Heaton, Chelune, Talley, Kay, & Curtis, 1993) was performed as the first or last (detailed description of neuropsychological indicators used in the study is presented in Online Appendix 1 of the Online Supplement).

Anthropometric Assessment and Overweight Diagnosis

Assessment of obesity was not the objective of the project from which the data used in the present study were collected. Measurements of height and body weight were performed additionally in the clinical evaluation of patients. Height measurements were performed according to standard procedures (de Onis, Onyango, Van den Broeck, Chumlea, & Martorell, 2004; Hanć, Janicka, Durda, & Cieślik, 2013) using stadiometer with precision to 1 mm. Body weight was measured using medical weight with precision to 100 g. Patients were weighed in their underwear. Measurements were performed by trained medical personnel. On the basis of the measurements, we calculated the body mass index (BMI). Height, weight, and BMI were standardized for age and sex based on Polish growth charts (Kułaga et al., 2010). These growth charts give standard measurements at an interval of 1 year; therefore, when calculating the z scores, we assumed a constant rate of change of the mean and standard deviation between 1 year and the next. Underweight, overweight, and obesity were diagnosed in patients under the guidance of the International Obesity Task Force (Cole, Bellizzi, Flegal, & Dietz, 2000; Cole, Flegal, Nicholls, & Jackson, 2007).

Statistical Methods

To compare the prevalence of overweight in patient subgroups distinguished on the basis of the category of the controlled variables, the Pearson’s chi-square test or the Fisher’s exact test were used. To evaluate the differences in neuropsychological tests between groups distinguished by the presence of overweight or alleles and genotypes, the ANOVA, Kruskal–Wallis test, or Mann–Whitney U tests were used, depending on the number of compared groups and whether the compared groups met the requirements of the parametric tests. The Tukey’s and Dunn’s post hoc tests were used to confirm the significance of differences between groups in case of multiple comparisons.

To isolate two patient groups, homogeneous due to the pattern of obtained results of neuropsychological tests, we applied the cluster analysis with the method of k-means on cases, after previous standardization of results of individual indicators to their own means and standard deviations.

The relationship of alleles and genotypes with overweight was evaluated with the help of the chi-square and Fisher’s exact tests. Stepwise logistic regression with Quasi-Newton estimation method was used to evaluate the strength of the relation between selected genotypes with overweight and obesity. Genotypes were tested in models unadjusted to other variables. All tests were considered to be statistically significant at p < .05 and not corrected for multiple testing. Statistica (Version 9) was applied for all statistical analyses. The power analysis was performed with the use of G*Power (Version 3.1).

Results

The database we used contained data of 205 patients. However, it was not possible to obtain the complete data for all the examined participants. Because of that, we excluded from the test those patients for whom we could not obtain the date of birth (necessary to calculate the exact age and for the standardization of the anthropometric measurements), full anthropometric data, or DNA sample. Due to a small number of girls with complete data, in our analyses, we only used data of boys (the procedure of data selection is presented in Figure S1 of the Online Supplement).

The final study sample consisted of 109 boys, aged between 7 and 17 years (M = 11.13, SD = 2.79). Basic characteristics of the sample are presented in Table 1. According to G*Power (Version 3.1), the sample size provides a 75% to 99% chance of detecting moderate to large effects in case of the chi-square test, 63% to 97% for ANOVA, and 0.57% to 0.99% for the t-student test if at a two-tailed alpha level of .05. For small effects, the power falls down to 12%, 14%, and 14%, respectively.

Table 1.

Basic Characteristics of the Sample.

	n	%
ADHD type
Combined	79	72.48
ADD	22	20.18
H/I	8	7.34
Comorbid disorders
At least one additional diagnosed disorder	101	92.66
Learning disorders	65	59.63
Oppositional defiant disorder	45	41.28
Speech disorders	34	31.19
Tic disorder	17	15.60
Conduct disorder	13	11.93
Enuresis	11	10.09
Anxiety disorders	8	7.34
Mood disorders	5	4.59
Treatment
Currently medicated	62	56.88
Place of residence
City with more than 100,000 citizens	65	59.63
City with less than 100,000 citizens	26	23.86
Villages	18	16.51
Mother’s level of education
Higher	26	23.85
Secondary	59	54.13
Vocational	20	9.17
Elementary	4	3.67
Father’s level of education
Higher	15	13.76
Secondary	50	45.87
Vocational	36	33.03
Elementary	8	7.34
Socioeconomic status
Very high	5	4.59
High	54	49.54
Average	45	41.28
Low	4	3.67
Very low	1	0.92
Birth term
<37 weeks of gestation	20	18.35
Between 37 and 42 weeks of gestation	71	65.14
>42 weeks of gestation	18	16.51
Birth body mass (g)
<2,500	13	11.93
2,500-4,000	87	79.82
>4,000	9	8.25
Apgar score
8-10	90	82.57
<8	19	17.43
Weight status (IOTF)
Underweight	1	0.92
Normal weight	84	77.06
Overweight	20	18.35
Obesity	4	3.67

Note. ADD = attention deficit disorder; H/I = hyperactive/impulsive type; IOTF = International Obesity Task Force.

Most of the boys had a combined type of ADHD (72.48%) and at least one comorbid disorder (92.66%). The most frequently recognized comorbid disorders in the group were the learning disorders (59.63%) and oppositional defiant disorder (41.28%). At the moment of recruitment, 56.88% of the boys were treated pharmacologically due to ADHD and/or comorbid disorders. The mean of body height adjusted for age and sex was 0.21 (95% confidence interval [CI] = [0.002, 0.40], SD = 1.00), for age and sex adjusted weight 0.08 (95% CI = [−0.1, 0.26], SD = 0.95), and for age and sex adjusted BMI 0.02 (95% CI = [−0.17, 0.21], SD = 0.99). Underweight was present in one boy (0.92%), overweight in 20 (18.35%), and obesity in four (3.6%). Due to a small number of boys with obesity, in all analyses presented below, we have evaluated the prevalence of overweight understood as abnormally elevated body weight (overweight + obesity). The prevalence of overweight was not dependent on controlled variables such as socioeconomic status, the date of birth, birth weight, Apgar score, ADHD subtype, symptoms intensity, pharmacological treatment, or comorbid disorders (results are described in detail in Appendix 2 of the Online Supplement).

Analysis of the Relationship Between Polymorphisms and Executive Functions

General results of neuropsychological tests are presented in Table S1 and the prevalence of genotypes and alleles of analyzed polymorphisms are shown in Table S2 of the Online Supplement.

The k-mean clustering analysis test was used to distinguish two clusters of patients with substantially different results of neuropsychological tests (see Appendix 3 and Table S3 of Online Supplement). Based on the configuration of the test results, we have concluded that Cluster 1 represents patients with a low level of executive functions (LLEF; lower scores for indicators: the first response time in MFFT, the number of words and the number of correct words in both parts of VFT, and with higher scores for all other indicators), and Cluster 2 represents patients with a high level of executive functions (HLEF; higher scores for indicators: the first response time in MFFT, the number of words and the number of correct words in both parts of VFT, and lower scores for all other indicators).

The Mann–Whitney U test was used to test the differences in the results of the neuropsychological tests obtained, depending on the present alleles and genotypes of studied polymorphisms. In the case of genotypes, the tests were carried out in the recessive (AA vs. Aa+aa) and the dominant (aa vs. Aa+AA) model. The relationship of alleles and genotypes with LLEF and HLEF were tested with chi-square or Fisher’s exact tests. All polymorphisms, except DAT VNTR, were significantly connected with the results of the neuropsychological tests (Table 2). Related to LLEF were rs1800443 (allele G: p = .02, genotype GT: p = .02) and rs1800955 (genotype TT: p = .02) of gene DRD4 and DAT rs463379 (allele C: p = .02, genotypes GC and CC: p = .01).

Table 2.

The Association of Polymorphisms With the Results of Neuropsychological Tests.

Polymorphism	Allele/genotype	Association with results of neuropsychological tests—Kruskal–Wallis test, Mann–Whitney U test, or χ² test
DRD2 rs1799732	insC	—
	delC	SC-WIT: The number of incorrect reactions in task C ↓ (p = .01)
	insC/insC	SC-WIT: The number of incorrect reactions in task C ↑ (p = .01)
	insC/delC	SC-WIT: The number of incorrect reactions in task C ↓ (p = .01)
DRD3 rs6280	T	SC-WIT: The time of task C performance ↓ (p = .01)
	C	—
	TT	SC-WIT: The time of Task A performance ↑ (p = .01), the time of Task C performance ↑ (p = .008); VFT-PhF: The number of perseverations ↓ (p = .01), VFT-CF: The number of words ↓ (p = .02), The number of perseverative errors ↓ (p = .03)
	TC	SC-WIT: The time of Task A performance ↑ (p = .01), the time of Task C performance ↑ (p = .008); VFT-PhF: The number of perseverations ↓ (p = .01), VFT-CF: The number of words ↓ (p = .02), The number of perseverative errors ↓ (p = .03)
	CC	SC-WIT: The time of Task A performance ↓ (p = .01), the time of Task C performance ↓ (p = .008); VFT-PhF: The number of perseverations ↑ (p = .01), VFT-CF: The number of words ↑ (p = .02), The number of perseverative errors ↑ (p = .03)
DRD4 rs1800443	G	CPT: The number of incorrect reactions ↑ (p = .03), the number of correct reactions ↓ (p = .02); SC-WIT: The time of Task A performance ↑ (p = .009), the time of Task C performance ↑ (p = .04); TMT: The time of Task A performance ↑ (p = .001); WCST: The number of perseverative errors ↑ (p = .003); LLEF (p = .02)
	T	—
	GT	CPT: The number of incorrect reactions ↑ (p = .03), the number of correct reactions ↓ (p = .02); SC-WIT: The time of Task A performance ↑ (p = .009), the time of Task C performance ↑ (p = .04); TMT: The time of task A performance ↑ (p = .001); WCST: The number of perseverative errors ↑ (p = .003); LLEF (p = .02)
	TT	CPT: The number of incorrect reactions ↓ (p = .03), the number of correct reactions ↑ (p = .02); SC-WIT: The time of Task A performance ↓ (p = .009), the time of Task C performance ↓ (p = .04); TMT: The time of Task A performance ↓ (p = .001); WCST: The number of perseverative errors ↓ (p = .003); HLEF (p = .02)
DRD4 rs1800955	C	SC-WIT: The number of incorrect reactions in Task A ↓ (p = .02), the number of incorrect reactions in Task C ↓ (p = .02); WCST: % conceptual level responses ↑ (p = .04); HLEF (p = .02)
	T	SC-WIT: The number of incorrect reactions in Task C ↑ (p = .02)
	CC	SC-WIT: The number of incorrect reactions in Task A ↓ (p = .02), the number of incorrect reactions in Task C ↓ (p = .02); WCST: % conceptual level responses ↓ (p = .04); HLEF (p = .02)
	CT	SC-WIT: The number of incorrect reactions in Task A ↓ (p = .02), the number of incorrect reactions in Task C ↓ (p = .02); WCST: % conceptual level responses ↓ (p = .04); HLEF (p = .02)
	TT	SC-WIT: The number of incorrect reactions in Task A ↑ (p = .02), the number of incorrect reactions in Task C ↑ (p = .02); WCST: % conceptual level responses ↑ (p = .04); LLEF (p = .02)
DRD4 48 VNTR	2	TMT: The time of Task B performance ↓ (p = .04)
	3	VFT-CF: The number of words ↑ (p = .03), the number of correct words ↑ (p = .04), the number of perseverations ↑ (p = .005)
	4	VFT-PhF: The number of perseverations ↓ (p = .04), VFT-CF: The number of incorrect words ↓ (p = .01)
	7	—
	2/4	SC-WIT: The number of incorrect reactions in Task B ↓ (p = .01); TMT: The time of Task B performance ↓ (p = .02)
	3/4	SC-WIT: The number of incorrect reactions in Task A ↑ (p = .02), the number of incorrect reactions in Task A ↑ (p = .02)
	4/4	ROCF: Reproduction—the time of task performance ↑ (p = .04)
	4/7	—
DAT rs27072	C	VFT-PhF: The number of perseverations ↓ (p = .01)
	T	VFT-PhF: The number of perseverations ↑ (p = .02), VFT-CF: The number of words ↑ (p = .02), the number of perseverative errors ↑ (p = .01)
	CC	VFT-PhF: The number of perseverations ↓ (p = .02), VFT-CF: The number of words ↓ (p = .02), the number of perseverative errors ↓ (p = .01)
	CT	VFT-PhF: The number of perseverations ↓ (p = .02), VFT-CF: The number of words ↑ (p = .02), the number of perseverative errors ↑ (p = .01)
	TT	VFT-PhF: The number of perseverations ↑ (p = .02), VFT-CF: The number of words ↑ (p = .02), the number of perseverative errors ↑ (p = .01)
DAT rs463379	G	HLEF (p = .03)
	C	SC-WIT: The number of incorrect reactions in Task A ↑ (p = .02), the number of incorrect reactions in Task B ↑ (p = .03), the number of incorrect reactions in Task C ↑ (p = .005); LLEF (p = .02)
	GG	SC-WIT: The number of incorrect reactions in Task A ↓ (p = .02), the number of incorrect reactions in Task B ↓ (p = .03), the number of incorrect reactions in Task C ↓ (p = .005); HLEF (p = .01)
	GC	SC-WIT: The number of incorrect reactions in Task A ↑ (p = .02), the number of incorrect reactions in Task B ↑ (p = .03), the number of incorrect reactions in Task C ↑ (p = .005); LLEF (p = .01)
	CC	SC-WIT: The number of incorrect reactions in Task A ↑ (p = .02), the number of incorrect reactions in Task B ↑ (p = .03), the number of incorrect reactions in Task C ↑ (p = .005); LLEF (p = .01)
DAT VNTR	9	—
	10	—
	9/9	—
	9/10	—
	10/10	—
COMT rs4680	A	CPT: The number of incorrect reactions ↓ (p = .02), VFT-CF: The number of perseverations ↑ (p = .03)
	G	—
	AA (Val/Val)	CPT: The number of incorrect reactions ↓ (p = .02); MFFT: The time of giving the first answer ↑ (p = .02); VFT-CF: The number of perseverations ↑ (p = .03)
	AG (Val/Met)	CPT: The number of incorrect reactions ↓ (p = .02); MFFT: The time of giving the first answer ↑ (p = .02); VFT-CF: The number of perseverations ↑ (p = .03)
	GG (Met/Met)	CPT: The number of incorrect reactions ↑ (p = .02); MFFT: The time of giving the first answer ↓ (p = .02); VFT-CF: The number of perseverations ↓ (p = .03)
SNAP25 rs363039	G	VFT-CF: The number of perseverations ↓ (p = .02)
	A	—
	GG	VFT-CF: The number of perseverations ↓ (p = .02)
	GA	VFT-CF: The number of perseverations ↓ (p = .02)
	AA	VFT-CF: The number of perseverations ↑ (p = .02)
SNAP25 rs363043	C	TMT: The time of task A performance ↑ (p = .02), the time of Task B performance ↑ (p = .008)
	T	CPT: Mean time of reaction ↑ (p = .02); ROCF: Copy—the time of task performance ↑ (p = .01)
	CC	CPT: Mean time of reaction ↓ (p = .03); TMT: The time of Task A performance ↑ (p = .02), the time of Task B performance ↑ (p = .009); ROCF: Copy—the time of task performance ↓ (p = .01)
	CT	CPT: Mean time of reaction ↑ (p = .03); TMT: The time of task A performance ↑ (p = .02), The time of task B performance ↑ (p = .009); ROCF: Copy—the time of task performance ↑ (p = .01)
	TT	CPT: Mean time of reaction ↑ (p = .03); TMT: The time of Task A performance↓ (p = .02), the time of Task B performance ↓ (p = .009); ROCF: Copy—the time of task performance ↑ (p = .01)
SNAP25 rs363050	A	—
	G	ROCF: Copy—scores ↑ (p = .03)
	AA	ROCF: Copy—scores ↓ (p = .03)
	AG	ROCF: Copy—scores ↑ (p = .03)
	GG	ROCF: Copy—scores ↑ (p = .03)
5HTR2A rs17288723	T	—
	C	VFT-PhF: The number of words ↑ (p = .02)
	TT	VFT-PhF: The number of words ↓ (p = .02)
	TC	VFT-PhF: The number of words ↑ (p = .02)
	CC	VFT-PhF: The number of words ↑ (p = .02)
BDNF rs6265	G	—
	A	WCST: The number of nonperseverative errors ↓ (p = .04)
	GG	WCST: The number of nonperseverative errors ↑ (p = .04)
	GA	WCST: The number of nonperseverative errors ↓ (p = .04)
	AA	WCST: The number of nonperseverative errors ↓ (p = .04)

Note. SC-WIT = The Stroop Color-Interference Test; VFT-PhF = The Verbal Fluency Test—phonological fluency; CPT = Continuous Performance Test; WCST = Wisconsin Card Sorting Test; LLEF = low level of executive functions; TMT = Trail Making Test; HLEF = high level of executive functions; VFT-CF = The Verbal Fluency Test—categorical fluency; ROCF = The Rey Complex Figure; MFFT = The Matching Familiar Figures Test; ↑ = positive association; ↓ = negative association.

Analyses of the Relationship of Polymorphisms With Overweight

Four alleles were statistically significantly less frequently present in the group of patients with overweight than in the group of patients without overweight: DRD4 rs1800955 allele T (72.73% vs. 90.48%, χ² = 4.79, p = .03), SNAP25 rs363039 allele A (41.67% vs. 64.71%, χ² = 4.13, p = .04), SNAP25 rs363043 allele C (75% vs. 92.94%, χ² = 6.15, p = .01), and 5HTR2A rs17288723 allele C (8.33% vs. 28.24%, χ² = 4.08, p = .04; Table 3).

Table 3.

The Differences in the Frequency of Studied Alleles Between Patients With and Without Overweight.

Polymorphism	Obesity	Allelen (%)	χ²	p	Allelen (%)	χ²	p
DRD2 rs1799732		insC			delC
	No	85 (100)	—	—	12 (14.12)	0.04	.84
	Yes	24 (100)			3 (12.50)
DRD3 rs6280		T			C
	No	79 (94.05)	0.17	.68	35 (41.67)	0.53	.47
	Yes	22 (91.67)			12 (50.00)
DRD4 rs1800443		G			T
	No	48 (60.00)	0.76	.38	80 (100)	—	—
	Yes	12 (50.00)			24 (100)
DRD4 rs1800955		C			T
	No	55 (65.48)	2.17	.14	76 (90.48)	4.79	.03
	Yes	18 (81.82)			16 (72.73)
DRD4 48 VNTR		2			3
	No	16 (19.28)	1.27	.26	6 (7.23)	0.09	.77
	Yes	2 (11.11)			2 (9.09)
		4			7
	No	72 (86.75)	0.35	.56	27 (32.53)	3.61	.06
	Yes	18 (81.82)			12 (54.55)
DAT rs27072		C			T
	No	83 (97.65)	0.57	.45	31 (36.47)	0.44	.51
	Yes	24 (100)			7 (29.17)
DAT rs463379		G			C
	No	81 (95.29)	1.17	.28	38 (44.71)	0.67	.41
	Yes	24 (100)			13 (54.17)
DAT VNTR		9			10
	No	35 (48.61)	0.01	.91	67 (93.06)	0.44	.51
	Yes	8 (47.06)			15 (88.24)
COMT rs4680		A			G
	No	63 (74.12)	0.26	.61	64 (75.29)	3.01	.08
	Yes	19 (79.17)			22 (91.67)
SNAP25 rs363039		G			A
	No	71 (83.53)	0.22	.64	55 (64.71)	4.13	.04
	Yes	21 (87.50)			10 (41.67)
SNAP25 rs363043		C			T
	No	79 (92.94)	6.15	.01	37 (4.53)	1.65	.20
	Yes	18 (75.00)			14 (58.33)
SNAP25 rs363050		A			G
	No	64 (75.29)	0.19	.66	57 (67.06)	1.35	.24
	Yes	17 (70.83)			13 (54.17)
5HTR2A rs17288723		T			C
	No	84 (98.82)	0.93	.33	24 (28.24)	4.08	.04
	Yes	23 (95.83)			2 (8.33)
BDNF rs6265		G			A
	No	85 (100)	3.57	.06	25 (29.41)	0.69	.41
	Yes	23 (95.83)			5 (20.83)

Note. The bold values represent the result of χ² with p < .05.

The tests have shown significant diversity in the prevalence of genotypes of four polymorphisms between boys with and without overweight: DRD4 rs1800955 (for table 2 × 2, p = .03), SNAP25 rs363039 (for table 2 × 2, p = .03), rs363043 (for table 3 × 2, p = .04; for table 2 × 2, p = .02), and 5HTR2A rs17288723 (for table 2 × 2, p = .04; for table 3 × 2, p = .03). The analysis of the logistic regression demonstrated that the CT and TT genotypes of rs1800955 polymorphism of gene DRD4, odds ratio (OR) = 0.28, p = .03, were associated with lower prevalence of overweight than CC, and SNAP25 rs363039 genotypes GA and AA, OR = 0.39, p = .04, were significantly associated with lower risk of overweight than GG, whereas SNAP25 rs363043 genotype TT was significantly associated with elevated risk of being obese relative to TC and CC (OR = 4.38, p = .02; Table 4).

Table 4.

The Differences in the Frequency of Genotypes Between Patients With and Without Overweight.

Polymorphism	Obesity	Genotypes				χ²	p	Fisher’s test—Tables 2 × 2 (p)Logistic regression analysis
Polymorphism	Obesity	1	2	3	4	χ²	p	1 × 2 + 3	1 + 2 × 3
DRD2 rs1799732		insC/insC	insC/delC	delC/delC
	No	73 (85.88)	12 (14.12)	0		0.04	.84	0.04 (0.57)	—
	Yes	21 (87.50)	3 (12.50)	0
DRD3 rs6280		TT	TC	CC
	No	49 (58.33)	30 (35.71)	5 (5.95)		0.57	.75	0.44 (0.33)	0.90 (0.32)
	Yes	12 (50.00)	10 (41.67)	2 (8.33)
DRD4 rs1800443		GG	GT	TT
	No	0	48 (60.00)	32 (40.00)		0.76	.38	—	0.40 (0.26)
	Yes	0	12 (50.00)	12 (50.00)
DRD4 rs1800955		CC	CT	TT
	No	8 (9.52)	47 (55.95)	29 (34.52)		5.66	.06	4.79 (0.03)	2.17 (0.11)
	Yes	6 (27.27)	12 (54.55)	4 (18.18)				OR = 0.28 95% CI: [0.08, 0.93] p = .03
DRD4 48 VNTR		2/4	3/4	4/4	4/7			2/4× others	3/4× others	4/4× others	4/7× others
	No	13 (18.06)	5 (6.94)	35 (48.61)	19 (26.39)	4.93	.18	1.86 (0.16)	0.07 (0.63)	1.62 (0.15)	2.89 (0.08)
	Yes	1 (5.88)	1 (5.88)	6 (35.29)	9 (52.94)
DAT rs27072		CC	CT	TT
	No	54 (63.53)	29 (34.12)	2 (2.35)		0.86	.65	0.44 (0.34)	0.57 (0.61)
	Yes	17 (70.83)	7 (29.17)	0
DAT rs463379		GG	GC	CC
	No	47 (55.29)	34 (40.00)	4 (4.71)		2.31	.31	0.67 (0.28)	1.17 (0.36)
	Yes	11 (45.83)	13 (54.17)	0
DAT VNTR		9/9	9/10	10/10
	No	5 (6.94)	30 (41.67)	37 (51.39)		0.55	.76	0.44 (0.40)	0.01 (0.56)
	Yes	2 (11.76)	6 (35.29)	9 (52.94)
COMT rs4680		AA (Val/Val)	AG (Val/Met)	GG (Met/Met)
	No	21 (24.71)	42 (49.41)	22 (25.88)		4.16	.12	3.01 (0.07)	0.26 (0.42)
	Yes	2 (8.33)	17 (70.83)	5 (20.83)
SNAP25 rs363039		GG	GA	AA
	No	30 (35.29)	41 (48.24)	14 (16.47)		4.20	.12	4.13 (0.03)	0.22 (0.45)
	Yes	14 (58.33)	7 (29.17)	3 (12.50)				OR = 0.39 95% CI: [0.15, 0.99] p = .04
SNAP25 rs363043		CC	CT	TT
	No	48 (56.47)	31 (36.47)	6 (7.06)		6.29	.04	1.64 (0.15)	6.15 (0.02)
	Yes	10 (41.67)	8 (33.33)	6 (25.00)					OR = 4.38 95% CI: [1.25, 15.41] p = .02
SNAP25 rs363050		AA	AG	GG
	No	28 (32.94)	36 (42.35)	21 (24.71)		2.48	.29	1.35 (0.18)	0.19 (0.42)
	Yes	11 (45.83)	6 (25.00)	7 (29.17)
5HTR2A rs17288723		TT	CT	CC
	No	61 (71.76)	23 (27.06)	1 (1.18)		6.34	.04	4.08 (0.03)	0.92 (0.39)
	Yes	22 (91.67)	1 (4.17)	1 (4.17)				OR = 0.2395% CI: [0.05, 1.08]p = .05
BDNF rs6265		GG (Val/Val)	GA (Val/Met)	AA (Met/Met)
	No	60 (70.59)	25 (29.41)	0		4.87	.09	0.69 (0.29)	3.57 (0.22)
	Yes	19 (79.17)	4 (16.67)	1 (4.17)

Note. The bold values represent the result of test with p < .05. OR = odds ratio; 95% CI = 95% confidence interval.

Neuropsychological Indicators and the Risk of Overweight

The Mann–Whitney U test was used to evaluate the differences in the level of the results of neuropsychological tests between patients with and without overweight. The analysis did not show any significant statistical differences between the groups (Table 5).

Table 5.

The Differences in Neuropsychological Tests Between Patients With and Without Overweight.

Test	Overweight
Test	NoM	YesM	U	p
Continuous Performance Test (n = 77)
Mean time of reaction (s)	780.66	756.61	521	0.91
The number of incorrect reactions	20.25	16.28	475.5	0.51
The number of correct reaction	38.73	39.28	503	0.74
The Stroop Color-Word Interference Test (n = 99)
The time of Task A performance (s)	111.22	102.83	851.5	0.43
The number of incorrect reactions in Task A	3.87	2.61	886	0.60
The time of Task B performance (s)	82.85	88.36	823	0.46
The number of incorrect reactions in Task B	1.86	0.91	735.5	0.17
The time of Task C performance (s)	153.53	144.71	713	0.37
The number of incorrect reactions in Task C	8.99	6.62	713	0.90
Trail Making Test (n = 93)
The time of Task A performance	47.52	44.61	780.5	0.31
The time of Task B performance	130.10	130.05	760.5	0.86
The Matching Familiar Figures Test (n = 106)
The time of giving the first answer (s)	13.93	12.18	858.5	0.46
The number of incorrect reactions	9.41	9.39	927.5	0.84
Impulsivity indicator	−0.83	−0.74	945.5	0.95
The Rey–Osterrieth Complex Figure (n = 56)
Copy—scores	24.50	23.82	270	0.66
Copy—the time of task performance (min)	3.41	3.09	242	0.39
Reproduction based on memory—scores	15.02	12.79	242.5	0.41
Reproduction based on memory—the time of task performance (min)	1.67	1.45	256.5	0.56
Verbal Fluency Test—phonological fluency (n = 105)
The number of words	9.45	9.30	908.5	0.79
The number of correct words	8.69	9.04	909	0.79
The number of perseverations	0.52	0.83	874	0.50
The number of incorrect words	0.23	0.04	798.5	0.08
Verbal Fluency Test—categorical fluency (n = 105)
The number of words	10.96	10.74	879.5	0.57
The number of correct words	10.34	10	867	0.50
The number of perseverations	0.18	0.30	846.5	0.21
The number of incorrect words	0.45	0.43	908	0.67
Wisconsin Card Sorting (n = 74)
The number of perseverative errors	16.16	19.33	373	0.09
The number of nonperseverative errors	13.79	16.17	436	0.39
% conceptual level responses	62.84	56.39	393	0.16
The number of cards needed to finish first category	4.91	4.50	413	0.21
Correct completed categories	14.71	16.67	499.5	0.96

Note. M = arithmetic mean, U = Mann–Whitney U test statistic.

The chi-square test was then used to evaluate the prevalence of overweight in clusters representing patients with low and high levels of executive functions. In this case as well, the differences between clusters were not statistically significant (LLEF: 20.51% vs. HLEF: 21.43%, χ² = 0.01, p = .91).

Discussion

The aim of the study was to evaluate the genetic and neuropsychological basis of the coexistence of ADHD and overweight. The study has indicated a relationship of overweight with polymorphisms DRD4 rs1800955, SNAP25 rs363039, rs363043, and 5HTR2A rs17288723, and strong association of most of the studied polymorphisms with the results of neuropsychological tests. Analysis did not show, however, any relationship of overweight with evaluated indicators of the level of executive functions.

In all, 13 out of 15 evaluated polymorphisms were connected with differences in the level of performance in the cognitive abilities tests. Polymorphisms of genes from the dopaminergic pathway, DRD4 rs1800443 and rs1800955, as well as DAT rs463379, had the most generalized effect on cognitive abilities, as evidenced by the differences in alleles or genotypes prevalence distribution between clusters distinguished by the level of executive functions (HLEF vs. LLEF; detailed discussion of the relationship between analyzed polymorphisms and executive functions have been described in Appendix 4 of the Online Supplement).

According to our knowledge, only two studies were carried out to test the relationship of neuropsychological test results with obesity in patients with ADHD. Graziano et al. (2011) evaluated 80 children and teenagers with ADHD (62 boys and 18 girls aged 4.5 to 18 years). The study revealed that children with ADHD and accompanying weight problems have the worst results in neuropsychological tests. Different results were obtained in the second study (Choudhry et al., 2013) on a larger sample (284 children: 210 boys and 74 girls with ADHD, aged 6 to 12 years). Children with normal weight, overweight, and obesity did not differ in terms of the level of test performance and motor activity. We obtained similar results in our study. The analyses we used did not indicate any important differences in the results of neuropsychological tests between patients with normal weight and those with overweight or obesity.

Because deficits of the evaluated executive functions do not explain the indicated relationship of polymorphisms with overweight in boys with ADHD, we must search for a different explanation. Advanced tests could evaluate mostly the “cool executive functions,” and only to a small degree, the “hot executive functions” that can be more strongly associated with increased food intake. Cool executive functions are described in the literature as goal and future-oriented skills, such as inhibition, flexibility, working memory, planning, and monitoring but which work in conditions void of context and not arousing any emotions. Hot executive functions are associated with completing tasks that arouse emotions, motivation, and tension, in which there is a choice between instant and delayed gratification (Peterson & Welsh, 2014). However, none of the tests we employed used any procedures that would, to a significant degree, activate any motivational-emotional processes in the patients.

Some of the studies indicate the presence of the reward-deficiency syndrome in both patients with ADHD (Blum et al., 1995; Heiligenstein & Keeling, 1995) and with obesity (Comings & Blum, 2000). This syndrome is connected not only with the tendency to choose immediate rewards and tendency toward risky behavior but also with improper diet. It is consistent with our hypothesis about engagement of hot executive functions in the development of overweight in boys with ADHD. Reward-deficiency syndrome is associated with alteration in the dopamine receptors DRD2 (Bazar, Yun, Lee, Daniel, & Doux, 2006) and DRD4 (Mitsuyasu et al., 2001; Tsai, Hong, Yu, & Chen, 2004), which are engaged in dopamine circuits of prefrontal attentional areas, impulse control, and reward pathways (Ariza et al., 2012); thereby, they can be connected with excessive weight gain in children and adults with ADHD (Cortese & Peñalver, 2010). Our study has confirmed the relationship of SNPs of DRD4 with overweight in the tested group. Other studies from recent years also indicated the involvement of not only DRD4 but also DAT1, in determining the development of excess body weight (Yokum, Marti, Smolen, & Stice, 2015). Among all, a relationship of DRD4 VNTR with the amount of food intake and the risk of obesity was demonstrated in children aged 3-5 and 7-8 years (Fontana et al., 2015; Silveira et al., 2014). However, among most recent studies on the genes of the dopaminergic system, there are also those that do not indicate a relationship of VNTR polymorphisms of genes DRD4 and DAT1 with obesity (Uzun et al., 2015). It is consistent with the results of our study, in which we demonstrated no association of VNTR polymorphisms of DRD4 and DAT1 with overweight in boys with ADHD.

Few studies have been conducted so far on the relationship of SNPs of SNAP25 with obesity. SNAP25 is a presynaptic plasma membrane protein, responsible for connecting synaptic vesicles with the neuron membrane, so that neurotransmitters can be secreted into the synaptic space. In a similar way, SNAP25 is connected with the regulation of insulin secretion by the pancreatic β cells (Takahashi et al., 2004). Studies published thus far have determined the relationship of polymorphisms of SNAP25 with the metabolic syndrome, including obesity and dysglycemia in patients with type 2 diabetes (Y. L. Chen et al., 2015) and in animal models (Valladolid-Acebes et al., 2015). Our studies are consistent with those results. We have demonstrated a relationship of SNPs of SNAP25 with a higher risk of overweight, which is connected perhaps with abnormalities in regulation of the sugar levels in blood.

Our study has also demonstrated a relationship of 5HTR2A rs17288723 with overweight in boys with ADHD. Many earlier studies have demonstrated a lowered food intake connected to increased serotonin signaling (Fox, French, Laporte, Blacker, & Murphy, 2010; Lam, Garfield, Marston, Shaw, & Heisler, 2010). The 5-HT_2A receptor regulates the production of glucose in the liver, the insulin secretion and glucose intake by the tissues through regulating the adrenaline level (Kokubu et al., 2006). The increased activity of 5-HT_2A leads to a higher blood sugar level and is often observed in patients with diabetes and obesity (Tsunekawa et al., 2003). The role of insulin level regulation, and thus the level of glucose in the blood, in conditioning the overweight in children with ADHD was confirmed in the latest studies. In recent years, the relationship of ADHD with a higher risk of type 2 diabetes, developing on the basis of insulin resistance, was demonstrated (H. J. Chen, Lee, Yeh, & Lin, 2013).

Limitation

The strength of this study is the inclusion of both genetic and neurocognitive factors as possible determinants of overweight in children with ADHD. In the study, we have also controlled other factors that could influence the obtained results, such as the type of ADHD, comorbid disorders, pharmacological treatment, socioeconomic status, parental education, and health status indicators in the neonatal period. The study, however, had some limitations. Among them were a small sample size and the lack of a control group. However, the study was powerful enough to detect large effects, and in the case of moderate effects, the power was relatively high (57%-75% depending on analyses type), whereas for small effects, it was definitely insufficient. The results (the level of p value) of the analyses were not corrected for multiple testing. It was justified by the exploratory character of the study. Nevertheless, all indicated relationships should be interpreted with caution, because of an increased probability of Type I error. Before definitive conclusions can be reached, independent replication based on adequately powered analyses on a larger sample should be done. Another limitation is that the evaluation of socioeconomic status was based on the subjective perception of the parents and not on objective indicators. Neuropsychological tests that we conducted could have engaged the cool executive functions to a higher degree, while the hot executive functions, connected to tasks arousing an emotional motivation, were not evaluated by adequate tests. In the study, we did not control for all other possible causes of overweight, such as the amount and caloric value of food intake, or limitation of physical activity. In the “Discussion” section, we refer to hormonal mechanisms that may underlie the overweight in the study group; we did not, however, have the data that could verify the hypotheses. Because the evaluation of the body size was not the main goal of the project, from which we have taken data we present in this article, the measurements of the body height and weight were performed as a part of the clinical evaluation of patients. Therefore, no procedures to evaluate the reliability of the measurements were planned. The measurement error remained outside control of our study.

Conclusion

We have demonstrated that overweight in boys with ADHD might be associated with SNPs of three candidate genes, that is, DRD4 rs1800955, SNAP25 rs363039, rs363043, and 5HTR2A rs17288723, however, not through conditioning of deficits in cool executive functions. Perhaps the tendency for overweight in people with ADHD might be connected to a lower level of hot executive functions and impaired control of absorption and metabolism of glucose. Those hypotheses require confirmation in next studies taking into account analyses of the levels of hormones and glucose in patients with ADHD.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The data used in the research were acquired from the project funded under a Grant of the Polish Ministry of Science and Higher Education no.: 2P05E 131 29.

Author Biographies

Tomasz Hanć, adjunct in Department of Human Biological Development, Institute of Anthropology, Faculty of Biology, Adam Mickiewicz University in Poznań, is an auxologist, psychologist, cognitive-behavioral therapist.

Monika Dmitrzak-Węglarz is an assistant professor in the Department of Psychiatric Genetics (Poznan University of Medical Sciences). A specialist in medical laboratory genetics, her current research interests include genetics background of psychiatric disorders, especially with disturbances of circadian rhythms and neurodevelopmental components. She is the author of more than 70 scientific publications.

Aneta Borkowska, neuropsychologist, is an associate professor at the Maria Curie-Skłodowska University in the Department of Clinical Psychology and Neuropsychology, Institute of Psychology. Her research focuses on disorders in child development resulting from various pathogenic factors and analyzed from the point of view of the relation between the brain and behavior (neuropsychological approach). She is a specialist in neuropsychological diagnostic assessments and therapy of children with neurodevelopmental disorders and acquired brain injuries.

Tomasz Wolańczyk is head of the Department of Child Psychiatry of Warsaw Medical University, Poland, since 2002. He is a specialist in neurology, child psychiatry, and a cognitive-bahavioral therapist, as well as author of 130 scientific publications.

Natalia Pytlińska, assistant in the Department of Child and Adolescent Psychiatry of Poznan University of Medical Sciences, is a specialist in child and adolescent psychiatry.

Filip Rybakowski is head of the Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Neurology, Warsaw, Poland. A specialist in child, adolescent, and adult psychiatry, he is the author of 130 scientific publications.

Radosław Słopień, assistant professor in the Department of Gynecological Endocrinology, Poznan University of Medical Sciences, is a specialist in obstetrics and gynecology, endocrinologist, and author of more than 60 publications.

Agnieszka Słopień, head of the Department of Child and Adolescent Psychiatry of Poznan University of Medical Sciences, Poland, since 2015, is a specialist in psychiatry, child, and adolescent psychiatry, a certified psychotherapist, and author of 130 scientific publications.

References

Agranat-Meged

Ghanadri

Eisenberg

Ben

N. Z.

Kieselstein-Gross

Mitrani-Rosenbaum

(2008). Attention deficit hyperactivity disorder in obese melanocortin-4receptor (MC4R) deficient subjects: A newly described expression of MC4R deficiency. American Journal of Medical Genetics: Part B, Neuropsychiatric Genetics, 147B, 1547-1553.

Albayrak

Ö.

Pütter

Volckmar

A. L.

Cichon

Hoffmann

Nöthen

M. M.

. . . Hinney

. (2013). Common obesity risk alleles in childhood attention-deficit/hyperactivity disorder. American Journal of Medical Genetics: Part B, Neuropsychiatric Genetics, 162, 295-305.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

Ariza

Garolera

Jurado

M. A.

Garcia-Garcia

Hernan

Sanchez-Garre

. . . Narberhaus

(2012). Dopamine genes (DRD2/ANKK1-TaqA1 and DRD4-7R) and executive function: Their interaction with obesity. PLoS ONE, 7, e41482. doi:10.1371/journal.pone.0041482

Bazar

K. A.

Yun

A. J.

Lee

P. Y.

Daniel

S. M.

Doux

J. D.

(2006). Obesity and ADHD may represent different manifestation of a common environmental oversampling syndrome: A model for revealing mechanistic overlap among cognitive, metabolic, and inflammatory disorders. Medical Hypotheses, 66, 263-269.

Bilgiç

Toker

Işık

Ü.

Kılıç

. (2016). Serum brain-derived neurotrophic factor, glial-derived neurotrophic factor, nerve growth factor, and neurotrophin-3 levels in children with attention-deficit/hyperactivity disorder. European Child & Adolescent Psychiatry. Advance online publication.

Blum

Sheridan

P. J.

Wood

R. C.

Braverman

E. R.

Chen

T. J.

Comings

E. D.

(1995). Dopamine D2 receptor gene variants: Association and linkage studies in impulsive-addictive-compulsive behaviour. Pharmacogenetics, 5, 121-141.

Borkowska

A. R.

(2008). Procesy uwagi i hamowania reakcji u dzieci z ADHD z perspektywy rozwojowej neuropsychologii klinicznej [Attentional processes and reaction inhibition in ADHD children from the Perspective of Developmental Clinical Neuropsychology]. Lublin, Poland: Wydawnictwo Uniwersytetu im. Marii Curie-Skłodowskiej w Lublinie.

Braida

Guerini

F. R.

Ponzoni

Corradini

De Astis

Pattini

. . . Sala

(2015). Association between SNAP-25 gene polymorphisms and cognition in autism: Functional consequences and potential therapeutic strategies. Translational Psychiatry, 5, e500.

10.

Briars

Todd

(2016). A review of pharmacological management of attention-deficit/hyperactivity disorder. The Journal of Pediatric Pharmacology and Therapeutics, 21, 192-206.

11.

Chen

H.-J.

Lee

Y.-J.

Yeh

G. C.

Lin

H. C.

(2013). Association of attention-deficit/hyperactivity disorder with diabetes: A population-based study. Pediatric Research, 73, 492-496.

12.

Chen

Y. L.

Pei

Hung

Y. J.

Lee

C. H.

Hsiao

F. C.

C. Z.

. . . Hsieh

C. H.

(2015). Associations between genetic variants and the severity of metabolic syndrome in subjects with type 2 diabetes. Genetics and Molecular Research, 14, 2518-2526.

13.

Choudhry

Sengupta

S. M.

Grizenko

Harvey

W. J.

Fortier

M.-E.

Schmitz

Joober

(2013). Body weight and ADHD: Examining the role of self-regulation. PLoS ONE, 8, e55351. doi:10.1371/journal.pone.0055351

14.

Cole

T. J.

Bellizzi

M. C.

Flegal

K. M.

Dietz

W. H.

(2000). Establishing a standard definition for child overweight and obesity worldwide: International survey. British Medical Journal, 320, 1240-1243.

15.

Cole

T. J.

Flegal

K. M.

Nicholls

Jackson

A. A.

(2007). Body mass index cut offs to define thinness in children and adolescents: International survey. British Medical Journal, 335, Article 194. doi:10.1136/bmj.39238.399444.55

16.

Comings

D. E.

Blum

(2000). Reward deficiency syndrome: Genetic aspects of behavioral disorders. Progress in Brain Research, 126, 325-341.

17.

Conners

C. K.

(1973). Rating scales for use in drug studies with children. Psychopharmacology Bulletin, 9, 24-84.

18.

Cordeiro

Siqueira-Roberto

Zung

Vallada

(2009). Association between the DRD2-141C insertion/deletion polymorphism and schizophrenia. Arquivos de Neuro-Psiquiatria, 7(2A), 191-194.

19.

Cortese

Moreira-Maia

C. R.

St. Fleur

Morcillo-Peñalver

Rohde

L. A.

Faraone

S. V.

(2015). Association between ADHD and obesity: A systematic review and meta-analysis. The American Journal of Psychiatry, 173, 34-43.

20.

Cortese

Peñalver

C. M.

(2010). Comorbidity between ADHD and obesity: Exploring shared mechanisms and clinical implications. Postgraduate Medicine, 122, 88-96.

21.

de Onis

Onyango

A. W.

Van den Broeck

Chumlea

W. C.

Martorell

. (2004). Measurement and standardization protocols for anthropometry used in the construction of a new international growth reference. Food and Nutrition Bulletin, 25(1 Suppl.), S27-S36.

22.

DuPaul

G. J.

Power

Anastopoulos

A. D.

Reid

(1998). ADHD Rating Scale–IV: Checklists, norms, and Clinical Interpretation. New York, NY: Guilford Press.

23.

Ettinger

Joober

DE Guzman

O’driscoll

G. A.

(2006). Schizotypy, attention deficit hyperactivity disorder, and dopamine genes. Psychiatry and Clinical Neurosciences, 60, 764-767.

24.

Fontana

Vitolo

M. R.

Campagnolo

P. D. B.

Mattevi

V. S.

Genro

Almeida

(2015). DRD4 and SLC6A3 gene polymorphisms are associated with food intake and nutritional status in children in early stages of development. Journal of Nutritional Biochemistry, 26, 1607-1612.

25.

Fox

M. A.

French

H. T.

Laporte

J. L.

Blacker

A. R.

Murphy

D. L.

(2010). The serotonin 5-HT(2A) receptor agonist TCB-2: A behavioral and neurophysiological analysis. Psychopharmacology, 212, 13-23.

26.

Friedel

Saar

Sauer

Dempfle

Walitza

Renner

. . . Hebebrand

(2007). Association and linkage of allelic variants of the dopamine transporter gene in ADHD. Molecular Psychiatry, 12, 923-933.

27.

Gizer

I. R.

Ficks

Waldman

I. D.

(2009). Candidate gene studies of ADHD: A meta-analytic review. Human Genetics, 126, 51-90.

28.

Gray

Yeo

G. S.

Cox

J. J.

Morton

Adlam

A. L.

Keogh

J. M.

. . . Faroogi

I. S.

(2006). Hyperphagia, severe obesity, impaired cognitive function and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes, 55, 3366-3371.

29.

Graziano

P. A.

Bagner

D. M.

Waxmonsky

J. G.

Reid

McNamara

J. P.

Geffken

G. R.

(2011). Co-occurring weight problems among children with attention deficit/hyperactivity disorder: The role of executive functioning. International Journal of Obesity, 36, 567-572. doi:10.1038/ijo.2011.245

30.

Hanć

Cieślik

Wolańczyk

Gajdzik

(2012). Assessment of growth in pharmacological treatment-naïve polish boys with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacolgy, 22, 300-306.

31.

Hanć

Janicka

Durda

Cieślik

(2013). An association between adverse events, anxiety and body size of adolescents. Journal of Biosocial Sciences, 29, 1-17.

32.

Hanć

Słopień

Wolańczyk

Dmitrzak-Węglarz

Szwed

Czapla

. . . Cieślik

(2015). ADHD and overweight in boys: Cross-sectional study with birth weight as a controlled factor. European Child & Adolescent Psychiatry, 24, 41-53.

33.

Hanć

Słopień

Wolańczyk

Szwed

Czapla

Durda

. . . Ratajczak

(2015). Attention-deficit/hyperactivity disorder is related to decreased weight in the preschool period and to increased rate of overweight in school-age boys. Journal of Child and Adolescent Psychopharmacolgy, 25, 691-700.

34.

Heaton

R. K.

Chelune

G. J.

Talley

J. L.

Kay

G. G.

Curtis

(1993). Wisconsin Card Sorting Test (WCST) manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources.

35.

Heiligenstein

Keeling

R. P.

(1995). Presentation of unrecognized attention deficit hyperactivity disorder in college students. Journal of American College Health, 43, 226-228.

36.

Horstmann

Lucae

Menke

Hennings

J. M.

Ising

Roeske

. . . Binder

E. B.

(2010). Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment. Neuropsychopharmacology, 35, 727-740.

37.

Jönsson

E. G.

Ivo

Forslund

Mattila-Evenden

Rylander

Cichon

. . . Sedvall

G. C.

(2001). No association between a promoter dopamine D(4) receptor gene variant and schizophrenia. American Journal of Medical Genetics, 105, 525-528.

38.

Kagan

Rosman

B. L.

Day

Albert

Phillips

(1964). Information processing in the child: Significance of analytic and reflective attitudes. Psychological Monographs, 78(1, Whole No. 578), 1-37.

39.

Kernie

S. G.

Liebl

D. J.

Parada

L. F.

(2000). BDNF regulates eating behavior and locomotor activity in mice. The EMBO Journal, 19, 1290-1300.

40.

Khalife

Kantomaa

Glover

Tammelin

Laiten

Ebeling

. . . Rodriguez

(2014). Childhood attention deficit/hyperactivity disorder symptoms are risk factors for obesity and physical inactivity in adolescence. Journal of the American Academy of Child & Adolescent Psychiatry, 53, 425-436.

41.

Kokubu

Tsuchihashi

Yuda

Hase

Eguchi

Wakabayashi

. . . Shimamoto

(2006). Persistent insulin-sensitizing effects of sarpogrelate hydrochloride, a serotonin 2A receptor antagonist, in patients with peripheral arterial disease. Circulation Journal, 70, 1451-1456.

42.

Kułaga

Różdżyńska

Palczewska

Grajda

Gurzkowska

Napieralska

, . . . OLAF Research Group. (2010). Siatki centylowe wysokości, masy ciała i wskaźnika masy ciała dzieci i młodzieży w Polsce—wyniki badania OLAF [Percentile charts of height, body mass and body mass index in children and adolescents in Poland—results of the OLAF study]. Standardy Medyczne/Pediatria, 7, 690-700.

43.

Lam

D. D.

Garfield

A. S.

Marston

O. J.

Shaw

Heisler

L. K.

(2010). Brain serotonin system in the coordination of food intake and body weight. Pharmacology Biochemistry & Behavior, 97, 84-91.

44.

Lezak

M. D.

(1995). Neuropsychological assessment. Oxford, UK: Oxford University Press.

45.

Lynos

W. E.

Mamonas

L. A.

Ricaurte

G. A.

Coppola

Reid

S. W.

Bora

S. H.

. . .Tessarollo

(1999). Brain-derived neurotropic factor-deficient mice develop aggressiveness and hyperphagia in conjunction with brain serotonergic abnormalities. Proceedings of the National Academy of Sciences of the United States of America, 96, 15239-15244.

46.

Mill

J. S.

Caspi

McClay

Sugden

Purcell

Asherson

. . . Moffitt

T. E.

(2002). The dopamine D4 receptor and the hyperactivity phenotype: A developmental-epidemiological study. Molecular Psychiatry, 7, 383-391.

47.

Miller

S. A.

Dykes

D. D.

Polesky

H. F.

(1988). A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Research, 16, 1215.

48.

Mitsuyasu

Hirata

Sakai

Shibata

Takeda

Ninomiya

. . . Fukumaki

(2001). Association analysis of polymorphism in the upstream region of the human dopamine D4 receptor gene (DRD4) with schizophrenia and personality traits. Journal of Human Genetics, 46, 26-31.

49.

Osterrieth

P. A.

(1944). Filetest de copie d’une figure complex: Contribution a l’etude de la perception et de la memoire [The test of copying a complex figure: A contribution to the study of perception and memory]. Archives de Psychologie, 30, 286-356.

50.

Peterson

Welsh

M. C.

(2014). The development of hot and cool executive functions in childhood and adolescence: Are we getting warmer? In Goldstein

Naglieri

J. A.

(Eds.), Handbook of executive functioning (pp. 45-65). New York, NY: Springer Science + Business Media.

51.

Racicka

Hanć

Giertuga

Bryńska

Wolańczyk

(2015). Prevalence of overweight and obesity in children and adolescents with attention-deficit/hyperactivity disorder: The significance of comorbidities and pharmacotherapy. Journal of Attention Disorders. Advance online publication. doi:10.1177/1087054715578272

52.

Reinert

K. R. S.

Po’e

E. K.

Barkin

S. L.

(2013). The relationship between executive function and obesity in children and adolescents: A systematic literature review. Journal of Obesity, 2013, Article 820956. doi:10.1155/2013/820956

53.

Reitan

(1985). Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation. Tucson, AZ: Reitan Neuropsychology.

54.

Ross

Hall

Smirnov

V. L.

(1998). High level multiplex genotyping by MALDI-TOF mass spectrometry. Nature Biotechnology, 16, 1347-1351.

55.

Rosvold

H. E.

Mirsky

A. F.

Sarason

Bransome

E. D.

Beck

L. H.

(1956). A continuous performance test of brain damage. Journal of Consulting Psychology, 20, 343-350.

56.

Silveira

P. P.

Portella

A. K.

Kennedy

J. L.

Gaudreau

Davis

Steiner

. . . Levitan

R. D.

(2014). Association between the seven-repeat allele of the dopamine-4 receptor gene (DRD4) and spontaneous food intake in pre-school children. Appetite, 73, 15-22.

57.

Słopień

(2011). Badania asocjacyjne genów kandydujących w zespole nadpobudliwości psychoruchowej i deficytu uwagi (ADHD) z wybranymi funkcjami poznawczymi [Candidate genes association studies in Attention-Deficit Hyperactivity Disorder (ADHD) with selected cognitive functions]. Poznań, Poland: Wydawnictwo Naukowe Uniwersytetu Medycznego im. K. Marcinkowskiego.

58.

Söderqvist

McNab

Peyrard-Janvid

Matsson

Humphreys

Kere

Klingberg

(2010). The SNAP25 gene is linked to working memory capacity and maturation of the posterior cingulate cortex during childhood. Biological Psychiatry, 68, 1120-1125.

59.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-662.

60.

Takahashi

Hatakeyama

Okado

Miwa

Kishimoto

Kojima

. . . Kasai

(2004). Sequential exocytosis of insulin granules is associated with redistribution of SNAP25. Journal of Cell Biology, 165, 255-262.

61.

Tsai

S. J.

Hong

C. J.

Y. W.

Chen

T. J.

(2004). Association study of catechol-O-methyltransferase gene and dopamine D4 receptor gene polymorphisms and personality traits in healthy young Chinese females. Neuropsychobiology, 50, 153-156.

62.

Tsunekawa

Hayashi

Suzuki

Matsui-Hirai

Kano

Fukatsu

. . . Iguchi

(2003). Plasma adiponectin plays an important role in improving insulin resistance with glimepiride in elderly type 2 diabetic subjects. Diabetes Care, 26, 285-289.

63.

Uzun

Saglar

Kucukyildirim

Erdem

Unlu

Mergen

(2015). Association of VNTR polymorphisms in DRD4, 5-HTT and DAT1 genes with obesity. Archives of Physiology & Biochemistry, 121, 75-79.

64.

Valladolid-Acebes

Daraio

Brismar

Harkany

Ögren

S. O.

Hökfelt

T. G.

Bark

(2015). Replacing SNAP-25b with SNAP-25a expression results in metabolic disease. Proceedings of the National Academy of Sciences of the United States of America, 112, 4326-4335.

65.

Vandenbergh

D. J.

Persico

A. M.

Hawkins

A. L.

GriYn

C. A.

Jabs

E. W.

Uhl

G. R.

(1992). Human dopamine transporter gene (DAT1) maps to chromosome 5p15.3 and displays a VNTR. Genomics, 14, 1104-1106.

66.

Willcutt

E. G.

Doyle

A. E.

Nigg

J. T.

Faraone

S. V.

Pennington

B. F.

(2005). Validity of the executive function theory of Attention-Deficit/Hyperactivity Disorder: A meta-analytic review. Biological Psychiatry, 57, 1336-1346.

67.

Wolańczyk

Kołakowski

(2005). Kwestionariusze do diagnozy ADHD i zaburzeń zachowania [The diagnostic structured interview for ADHD and conduct disorder]. Warsaw, Poland: Janssen-Cilag.

68.

Yang

J. W.

Jang

W. S.

Hong

S. D.

Y. I.

Kim

D. H.

Park

. . . Joung

Y. S.

(2008). A case-control association study of the polymorphism at the promoter region of the DRD4 gene in Korean boys with attention deficit-hyperactivity disorder: Evidence of association with the -521 C/T SNP. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 32, 243-248.

69.

Yang

Mao

Zhang

Zhao

(2013). Prevalence of obesity and overweight among Chinese children with attention deficit hyperactivity disorder: A survey in Zhejiang Province, China. BMC Psychiatry, 13, Article 133. doi:10.1186/1471-244X-13-133

70.

Yokum

Marti

C. N.

Smolen

Stice

(2015). Relation of the multilocus genetic composite reflecting high dopamine signaling capacity to future increases in BMI. Appetite, 87, 38-45.

71.

Zainal Abidin

Tan

E. L.

Chan

S. C.

Jaafar

Lee

A. X.

Abd Hamid

M. H.

. . . Mohamed Ibrahim

(2015). DRD and GRIN2B polymorphisms and their association with the development of impulse control behaviour among Malaysian Parkinson’s disease patients. BMC Neurology, 15, 59. doi: 10.1186/s12883-015-0316-2