Dopaminergic and Noradrenergic Gene Polymorphisms and Response to Methylphenidate in Korean Children with Attention-Deficit/Hyperactivity Disorder: Is There an Interaction?

Abstract

Objective:

We aimed to investigate the independent and interaction effects of dopamine transporter gene (DAT1), dopamine D4 receptor gene (DRD4), alpha-2A adrenergic receptor gene (ADRA2A), and norepinephrine transporter gene (NET1), with regard to treatment response to methylphenidate (MPH) in attention-deficit/hyperactivity disorder (ADHD).

Methods:

The participants of the study were 103 children and adolescents (ages 9.1±2.1 years) diagnosed as having ADHD according to American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) criteria. They were enrolled in an 8-week, open-label trial of MPH. The good responder group was defined as subjects having an ≥50% decrease in the ADHD Rating Scale-IV (ADHD-RS) total score from the baseline, and at the same time a Clinical Global Impressions-Improvement Scale (CGI-I) score of 1 or 2, both at the 8th week of MPH treatment. Multivariate stepwise logistic regression was performed to examine the independent and interaction effects of genotypes on the dichotomized MPH treatment response.

Results:

Significant interaction effects on MPH response were detected between the genotypes of the DRD4 variable number of tandem repeat (VNTR) polymorphisms and those of either the ADRA2A DraI or the NET1 −3081(A/T) polymorphisms; significant interaction effects were also detected between the genotypes of the ADRA2A DraI polymorphisms and those of either the NET1 G1287A or the NET1 −3081(A/T) polymorphisms (Nagelkerke R² =0.40). No significant independent effect of a genotype was detected according to the stepwise logistic regression results.

Conclusion:

The results suggest that genes involved in the dopaminergic and noradrenergic systems might interact to form important predictors of short-term response to MPH.

Introduction

A ttention-deficit/hyperactivity disorder (ADHD) is a heritable disorder characterized by inattention and/or hyperactivity/impulsivity, estimated to affect ∼ 3–7% of the school-age population, according to Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) criteria (American Psychiatric Association 1994). Its leading candidate genes include those involved in dopaminergic or noradrenergic neurotransmission, and these genes have also been implicated in the response to ADHD medication (Froehlich et al. 2010). Methylphenidate (MPH) is one of the most frequently prescribed first-line therapeutic agents for ADHD, which is reportedly effective in ∼ 70% of children with the disorder (Santosh and Taylor 2000). Its therapeutic action involves not only the dopaminergic but also the noradrenergic neurotransmitter systems, and, accordingly, dopaminergic and noradrenergic genes have been implicated in modifying the therapeutic response to this medication (Wilens 2008). As for dopamine, the variable number of tandem repeat (VNTR) located on the 3′ untranslated region (3′-UTR) of the dopamine transporter gene (DAT1) and the VNTR in exon III of the dopamine D4 receptor gene (DRD4) are the two polymorphic systems most studied in relation to MPH response: homozygosity of the 10-repeat allele at DAT1 has been replicated, including in Korean subjects, to be associated with a poor response to MPH (Winsberg and Comings 1999; Roman et al. 2002; Cheon et al. 2005; Kooij et al. 2008), although opposing results have been observed (Kirley et al. 2003; Bellgrove et al. 2005; Stein et al. 2005; McGough et al. 2006; Mick et al. 2006; Zeni et al. 2007), and for the latter, 7-repeat allele was associated with a poor response to MPH (Hamarman et al. 2004), but this allele is rare in East Asian populations, and in Korean children with ADHD the most frequent 4-repeat allele at DRD4 was shown to be associated with better treatment response to MPH (Cheon et al. 2007).

Fewer studies have investigated the effect of noradrenergic gene polymorphisms on treatment response to MPH. Presence of the G allele at the alpha-2A adrenergic receptor gene (ADRA2A) C-1291G polymorphism (an MspI polymorphism) was associated with greater improvement with MPH treatment (Polanczyk et al. 2007; da Silva et al. 2008; Cheon et al. 2009). However, one recent study has reported that both the MspI and DraI polymorphisms of the ADRA2A were not significantly associated with MPH response (Contini et al. 2011), although formerly implicated in the etiology of ADHD (Cho et al. 2008a). Meanwhile, presence of the G allele at the norepinephrine transporter gene (NET1) G1287A polymorphism was associated with favorable response to MPH (Yang et al. 2004). Then only recently, our group has reported one of the first studies on the association between the NET1 −3081(A/T) polymorphism and MPH treatment response, which showed that subjects with ADHD with the T allele at this polymorphism site had a better response than those without the T allele; this study showed, however, that the genotypes of the NET1 G1287A polymorphism were not significantly associated with MPH response (Kim et al. 2010). Subsequent pharmacogenetic studies of Korean subjects with ADHD also showed mixed results (Lee et al. 2011; Song et al. 2011).

From the perspective of neurotransmitter systems, the dopamine D4 receptor is known to have a higher affinity for norepinephrine than for dopamine, and norepinephrine in turn exhibits a higher affinity for the D4 receptor than for any of the adrenergic receptors (Van Tol et al. 1991; Prince 2008). And in the frontal cortex, dopamine transporter density is low and it is unlikely that dopamine transporter blockade accounts for the elevation of dopamine levels in this brain region; instead, norepinephrine transporter density is relatively high in this region and dopamine affinity for the norepinephrine transporter has also been reported to be higher than for the dopamine transporter, which suggest that norepinephrine transporter might be a major contributor to dopamine sequestration and recycling in the frontal cortex (Madras et al. 2005). As an example, atomoxetine selectively blocks the norepinephrine transporter but then increases the concentrations of both norepinephrine and dopamine in the prefrontal cortex (Bymaster et al. 2002). Moreover, therapeutic doses of MPH that improved cognitive function in rats concurrently increased not only dopamine but also norepinephrine in their prefrontal cortices (Berridge et al. 2006).

As the results of former studies have demonstrated varying degrees of inconsistency, and considering the hypothesized mechanisms for the interaction between the dopaminergic and noradrenergic systems in ADHD brains (Prince 2008; Wilens 2008), along with the fact that the two current categories of first-line pharmacotherapeutic agents for ADHD (stimulants and atomoxetine) mainly target these two neurotransmitter systems, we aimed to investigate the independent and interaction effects of selected polymorphisms at four major candidate genes for ADHD, namely DAT1, DRD4, ADRA2A, and NET1, with regard to treatment response to MPH.

Methods

Subjects

The participants of the study were stimulant-naïve children and adolescents recruited from the Department of Psychiatry at the Seoul National University Hospital in Korea, and then diagnosed as having ADHD according to the DSM-IV criteria. Subjects with intelligence quotient (IQ) < 70, or either currently diagnosed with tic disorder; obsessive-compulsive disorder; language disorder; learning disorder; or convulsive disorder; or having past and/or ongoing history of pervasive developmental disorder, schizophrenia, bipolar disorder, or brain damage were excluded. This study was approved by the institutional review board for human subjects at the Seoul National University Hospital. All the children and adolescents provided oral assent to participate in the study, and their parents provided written informed consent prior to study entry.

Diagnostic and clinical evaluations

The Kiddie Schedule for Affective Disorders and Schizophrenia for School-Aged Children—Present and Lifetime Version (K-SADS-PL) is a semistructured diagnostic interview tool based on the DSM-IV criteria. In this study, the diagnosis of ADHD and other comorbidities were based on the K-SADS-PL; the validity and reliability of its Korean version as well as the original version have been proven (Kaufman et al. 1997; Kim et al. 2004).

The ADHD Rating Scale-IV (ADHD-RS) consists of 18 items derived from the 18 symptom list of the DSM-IV criteria for ADHD (DuPaul et al. 1998). Each item is rated from 0 (never or rarely) to 3 (very often), therefore the total score ranges from 0 to 54. The validity and reliability of its Korean version as well as the original version have been established (So et al. 2002). Also, the Clinical Global Impressions-Improvement Scale (CGI-I) has been widely used to rate the overall symptom improvement of a disorder on a seven-point scale ranging from 1 (much improved) to 7 (much worse) (Guy 1976). The tool was standardized in Korean and its validity and reliability was established for ADHD. Furthermore, we had established high inter-rater reliability before the beginning of this study (κ=0.89). In this study, the ADHD-RS was rated by a parent before and after 8 weeks of treatment, and the CGI-I by a psychiatrist after 8 weeks; the raters were blind to the subject's genotypes.

MPH administration and definition of treatment response

The participants were enrolled in an 8-week, open-label trial of MPH. Initial doses of MPH were maintained for 2 weeks, and the doses were adjusted at the 2nd and the 4th week of treatment. The doses were titrated upward until sufficient therapeutic effects were achieved, on the basis of the subjects' and the parents' reports of symptom improvement and adverse effects, and then the doses were maintained for the remainder of the 8 weeks.

The good responder group was defined as subjects having an ≥50% decrease in the ADHD-RS total score from the baseline, and at the same time a CGI-I score of 1 or 2, both at the 8th week of MPH treatment (Lee et al. 2011). The other participants were allotted to the poor responder group.

Genotyping of dopamine-related genes

Genomic DNA was extracted from blood lymphocytes using a Genomic DNA Extraction Kit (Bioneer, Korea). DAT1 was localized on chromosome 5p15.3. The 40-base pair VNTR polymorphism located in the 3′-UTR of DAT1 was genotyped, as previously described (Kim et al. 2006). T7-5 Long [5′-TGT GGT GTA GGG AAC GGC CTG AG-3′] and T7-3a Long [5′-CTT CCT GGA GGT CAC GGC TCA AGG-3′] were used at concentrations of 5×10⁻⁷ M. The polymerase chain reaction (PCR) was performed under universal conditions, in a volume of 20 μL containing 10×PCR buffer (Applied Biosystems, CA), 1.5 mM MgCl2, 0.25 mM of each dNTP, 1.25 U AmpliTaq gold (Applied Biosystems), primers and 40 ng of genomic DNA. The reaction consisted of denaturation at 94°C for 4 minutes, followed by 35 cycles of 94°C for 40 seconds, 72°C for 40 seconds, and 72°C for 1 minute, with a final extension at 72°C for 10 minutes. Thermal cycling was performed on the PTC-100 Peltier Thermal cycler (MJ Research, MA) and the reaction products separated by 2% LE gel electrophoresis.

For the DRD4 exon III VNTR polymorphism (Cheon et al. 2007), the oligonucleotide primers [5′-ACC ACC ACC GGC AGG ACC CTC ATG GCC TTG CGC TC-3′ and 5′-CTT CCT ACC CTG CCC GCT CAT GCT GCT GCT CTA CTG G-3′] were used to generate the DRD4 exon III polymorphic region (2–10 variable repeat units, 1 unit=48 base pairs). The PCR amplification was performed with a volume of 20 μL containing 100 ng of genomic DNA, 10 pmol of each primer, 1×Pfu PCR buffer (Solgent, Korea), 400 μM deoxyadenosine triphosphate (dATP), deoxythymidine triphosphate (dTTP), and deoxycytidine triphosphate (dCTP), 200 μM deoxyguanosine triphosphate (dGTP) (Solgent, Korea), 200 μM 7-deaza-dGTP (Boehringer Mannheim), 5% Dimethyl sulfoxide (DMSO), and 2 U SolGent™ Pfu Taq. The reaction consisted of a denaturation step at 98°C for 5 minutes, followed by 35 cycles consisting of 98°C for 45 seconds, 55°C for 45 seconds, and 72°C for 1 minute 30 seconds, with a final extension step at 72°C for 5 minutes. Thermal cycling was performed on the PTC-100 Peltier Thermal cycler (MJ Research, MA). The amplification products were electrophoresed on a 2% agarose gel and visualized by ethidium bromide (EtBr) staining.

Genotyping of norepinephrine-related genes

Genomic DNA was extracted from whole blood lymphocytes using a G-DEX^™ II Genomic DNA Extraction Kit (Intron, Korea). The detection of single nucleotide polymorphisms (SNPs) was based upon analysis of primer extension products generated from previously amplified genomic DNA, using a chip-based matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry platform (Sequenom, CA). The ADRA2A and NET1 polymorphisms were genotyped as previously described (Cho et al. 2008a; Kim et al. 2010. Oligonucleotide primers [5′-ACG TTG GAT GTT CTC CCA AGA TCC AGC TTC and 5′-ACG TTG GAT GCC TGC TGG GAG TTG GCC AT for the ADRA2A MspI (rs1800544) polymorphism; 5′-ACG TTG GAT GCT AAT TCC CCT TCC ATT CCC and 5′-ACG TTG GAT GGT GTA TAT TTA CAG CGG GG for the ADRA2A DraI (rs553668) polymorphism; 5′-ACG TTG GAT GAG ACC CTA ATT CCT GCA CCC, and 5′-ACG TTG GAT GTT CAG GAC CTG GAA GTC ATC for the NET1 G1287A polymorphism; 5′-ACG TTG GAT GGT TTT CTT GCC CCT CAA GTG, and 5′-ACG TTG GAT GAG GGA AGG AAA CCA GGA GAA for the NET1 −3081(A/T) polymorphism] were used to generate PCR products. The PCR was performed in a volume of 5 μL containing 1×PCR buffer (TAKARA, Japan), 2.5 mM MgCl2, 0.2 mM of each dNTP, 0.1 U HotStar Taq Polymerase (Qiagen, Germany), 8 pM of each primer, and 4.0 ng of genomic DNA. The reaction consisted of denaturation at 95°C for 15 minutes, followed by 45 cycles at 95°C for 20 seconds, 56°C for 30 seconds, and 72°C for 1 minute, with a final extension at 72°C for 3 minutes. Following the PCR, unincorporated dNTPs were removed by adding 0.3 U of shrimp alkaline phosphatase (SAP) and incubating for 20 minutes at 37°C, followed by 5 minutes at 85°C for enzyme inactivation. The total volume of each reaction was 9 μL, including histone modifying enzyme (HME) (Thermosequenase, GE Healthcare, UK), ACT termination mix, and 5 μM of extension primer. The primer extension protocol was started at 94°C for 2 minutes, followed by 55 cycles at 94°C for 5 seconds, 52°C for 5 seconds, and 72°C for 5 seconds. After desalting of the reaction products with SpectroCLEAN (Sequenom), samples were analyzed in the fully automated mode with the MALDI-TOF MassARRAY system (Bruker-Sequenom, CA). Genotypes with unsure identification were excluded from the ensuing statistical analyses. Parts of the data on the norepinephrine-related genes have been published previously (Kim et al. 2010).

Statistical analyses

The allele frequencies were estimated by counting, and the Hardy–Weinberg equilibrium was tested using the goodness-of-fit χ² test. Then, for each polymorphism, different genotypes were grouped into two variables in accordance with previous studies (Cheon et al. 2005, 2007; Cho et al. 2008a,b; Kim et al. 2010). Descriptive statistics for categorical and continuous variables were analyzed using χ² (or Fisher's exact) tests and Student's t tests, respectively. The genotype distributions were compared between the good and poor responders using χ² (or Fisher's exact) tests. Multivariate logistic regression was performed to examine the independent and interaction effects of genotypes on the dichotomized MPH treatment response. The statistical model included terms for each genetic polymorphism and at the same time terms for each pairwise interaction of these polymorphisms; then stepwise variable selection procedure using backward elimination (Wald) was employed, with a significance level for removal from the model set as 0.10. All reported probabilities were two tailed, and an α level of 0.05 was considered statistically significant. All these statistical analyses were performed with SPSS 17.0 for Windows (SPSS Inc., Chicago, IL).

Results

Demographic and clinical characteristics

This study originally included 112 children and adolescents with ADHD, but 9 of them dropped out before completing the 8 week MPH trial, and the remaining 103 were included in the main analyses (Table 1). No baseline differences were found between the good and poor responder groups (n=24 and 79, respectively) in their demographic and clinical characteristics, except in their mean ADHD-RS total score. This difference, however, did not lead to significant differences in mean MPH dosage. The final ADHD-RS score and the CGI-I score were both significantly higher in the poor responder group, as expected.

Table 1.

Demographic and Clinical Characteristics of the Subjects

		Response to MPH
	ADHD	Poor	Good
	n=103	n=79	n=24	p Value
Age, mean (SD)	9.1 (2.1)	8.9 (1.9)	9.6 (2.2)	0.13
IQ, mean (SD)	107.4 (13.7)	107.2 (13.7)	106.6 (14.8)	0.87
ADHD subtypes, n (%)
Combined	69 (61.6%)	49 (62.0%)	15 (62.5%)	0.96
Inattentive	29 (25.9%)	22 (27.8%)	7 (29.2%)	0.90
Hyperactive-impulsive	6 (5.4%)	4 (5.1%)	1 (4.2%)	1.00
Not otherwise specified	8 (7.1%)	4 (5.1%)	1 (4.2%)	1.00
Comorbid disorders, n (%)
Oppositional defiant disorder	15 (13.4%)	13 (16.5%)	2 (8.3%)	0.51
Anxiety disorder	12 (10.7%)	9 (11.4%)	3 (12.5%)	1.00
Enuresis	5 (4.5%)	4 (5.1%)	1 (4.2%)	1.00
Baseline ADHD-RS score, mean (SD)	26.9 (10.2)	25.4 (9.6)	31.1 (10.8)	0.01
Final ADHD-RS score, mean (SD)	17.2 (8.8)	19.7 (8.5)	9.4 (4.4)	0.00
CGI-I score, mean (SD)	2.4 (0.8)	2.7 (0.8)	1.7 (0.4)	0.00
Initial MPH dosage (mg/day), mean (SD)	19.8 (8.2)	19.9 (8.6)	20.6 (8.2)	0.75
Final MPH dosage (mg/day), mean (SD)	29.1 (11.6)	29.9 (12.2)	27.9 (8.5)	0.44

ADHD=attention-deficit/hyperactivity disorder; ADHD-RS=ADHD Rating Scale-IV; CGI-I=Clinical Global Impressions-Improvement; IQ=intelligence quotient; MPH=methylphenidate; SD=standard deviation.

For the 9 subjects who dropped out, we could not collect their final ADHD-RS scores, and the design of this study did not include collection of side-effect profiles. Possible reasons for dropping out were not systematically assessed, and therefore we could not clarify whether they dropped out because of treatment inefficacy or intolerance, or both. However, we have retrospectively reviewed our research documents and found that two subjects experienced loss of appetite after MPH medication, and one of them also complained about insufficient effect. Another subject was documented to experience insomnia, and was also described to be hyperactive at the last visit before dropping out, suggesting insufficient medication effect as well. Regarding the fourth subject comments were made that his parent was very anxious about the child taking psychiatric medication. For the other five subjects, possible reasons for dropping out were not further clarified. The genotype distributions were not significantly different between the completers and the dropouts according to Fisher's exact tests.

Genetic polymorphisms and their independent association with MPH response

The genotype frequencies of the polymorphisms are briefly presented in Table 2. The distributions of the genotypes were all in agreement with the expected values of the Hardy–Weinberg equilibrium (p>0.05). Independent associations of genotype with MPH response based on χ² (or Fisher's exact) tests are also described in Table 2, and only the norepinephrine transporter polymorphisms showed a significant or a trend of association.

Table 2.

Independent Associations between Genotypes and Response to MPH Treatment

	Response to MPH
Genotype	Poor n (% within genotype / within total)	Good n (% within total)	Total	p Value	Phi
DAT1 VNTR				0.37	0.09
W/O 10/10	13 (68.4 / 12.6%)	6 (31.6 / 5.8%)	19 (18.4%)
With 10/10	66 (78.6 / 64.1%)	18 (21.4 / 17.5%)	84 (81.6%)
DRD4 VNTR				0.91	0.01
W/O 4/4	24 (77.4 / 23.3%)	7 (22.6 / 6.8%)	31 (30.1%)
With 4/4	55 (76.4 / 53.4%)	17 (23.6 / 16.5%)	72 (69.9%)
ADRA2A MspI				0.87	0.01
G/G	38 (76.0 / 36.9%)	12 (24.0 / 11.7%)	50 (48.5%)
G/C+C/C	41 (77.4 / 39.8%)	12 (22.6 / 11.7%)	53 (51.5%)
ADRA2A DraI				0.77	0.02
C/C	25 (75.8 / 24.5%)	8 (24.2 / 7.8%)	33 (32.4%)
C/T+T/T	54 (78.3 / 52.9%)	15 (21.7 / 14.7%)	69 (67.6%)
NET1 G1287A				0.08	0.16
G/G	37 (69.8 / 35.9%)	16 (30.2 / 15.5%)	53 (51.5%)
G/A+A/A	42 (84.0 / 40.8%)	8 (16.0 / 7.8%)	50 (48.5%)
NET1 −3081(A/T)				0.02	0.22
A/A	25 (92.6 / 24.3%)	2 (7.4 / 1.9%)	27 (26.2%)
A/T+T/T	54 (71.1 / 52.4%)	22 (28.9 / 21.4%)	76 (73.8%)

ADRA2A=alpha-2A adrenergic receptor gene; DAT1=dopamine transporter gene; DRD4=dopamine D4 receptor gene; MPH=methylphenidate; NET1=norepinephrine transporter gene; VNTR=variable number of tandem repeat.

Associations between genotypes and MPH response

Statistically significant genotypic effects are summarized in Table 3. Significant interaction effects on MPH response were detected between the genotypes of the DRD4 VNTR polymorphisms and those of either the ADRA2A DraI or the NET1 −3081(A/T) polymorphisms; significant interaction effects were also detected between the genotypes of the ADRA2A DraI polymorphisms and those of either the NET1 G1287A or the NET1 −3081(A/T) polymorphisms. In addition, trends of interaction effect were detected between the genotypes of the DAT1 VNTR and the NET1 −3081(A/T) polymorphisms; and also between the genotypes of the DRD4 VNTR and the ADRA2A MspI polymorphisms. No significant independent effect of a certain genotype was detected according to the stepwise logistic regression results. The combined group sizes in Table 3 and number of poor and good responders in each combined genotype group are further detailed in Appendix Table A1.

Table 3.

Predictors of Response to MPH Treatment

Predictor	B	SE	OR	p Value ^b	95% CI
DRD4 VNTR×ADRA2A DraI^a	−5.637	1.616	0.003	0.0004	0.000	0.084
DRD4 VNTR×NET1 −3081(A/T)	3.285	1.285	26.718	0.0105	2.151	331.879
ADRA2A DraI×NET1 G1287A^a	−2.211	0.815	0.110	0.0066	0.022	0.540
ADRA2A DraI×NET1 −3081(A/T)^a	5.203	1.462	181.855	0.0003	10.363	3191.049
DAT1 VNTR×NET1 −3081(A/T)	−1.388	0.731	0.250	0.0575	0.060	1.045
DRD4 VNTR×ADRA2A MspI	1.301	0.686	3.673	0.0577	0.958	14.085
Test			df			p Value
Goodness of fit (Hosmer-Lemeshow)	4.698		8			0.789
Nagelkerke R²	0.406					<0.001

Significant after correction for the number of polymorphisms tested (threshold: p=0.05/6[SNPs]=0.0083).

Sufficient numbers of decimal places are shown to allow correction for multiple comparisons.

ADRA2A=alpha-2A adrenergic receptor gene; B=unstandardized regression coefficient; CI=confidence interval; DAT1=dopamine transporter gene; df=degree of freedom; DRD4=dopamine D4 receptor gene; MPH=methylphenidate; NET1=norepinephrine transporter gene; OR=odds ratio; SE=standard error; SNP=single nucleotide polymorphism; VNTR=variable number of tandem repeat.

In our demographic analyses, baseline ADHD-RS scores were significantly different between the two responder groups. Therefore, we have conducted an additional multivariate logistic regression analysis including the baseline ADHD-RS score as a covariate. As a result, among the four significant pairwise interactions described previously, all remained significant except for the interaction between the DRD4 VNTR and the NET1 −3081(A/T) polymorphisms (p<0.01 in all three significant combinations; Nagelkerke R² =0.40). And no independent variable, or interaction of variables, was newly found to be significant, except the baseline ADHD-RS score (OR=1.07, p=0.01, 95% CI=1.01–1.14).

We have further tested whether our results remained consistent even if treatment response was defined otherwise. In order to define it as a continuous phenotype, we performed a complementary multivariable regression analysis using the CGI-I scores alone as a measure of MPH response: here, lower CGI-I scores reflect greater response (Appendix Table A2). The results showed two similar significant interactions (DRD4 VNTR×ADRA2A MspI; ADRA2A DraI×NET1 G1287A) and one newly found significant interaction (DAT1 VNTR×NET1 G1287A), as well as one newly found independent association (NET1 G1287A), compared with the results of the original main analysis.

Discussion

The present study has found that dopaminergic and noradrenergic genotypes interact in association with different treatment responses to MPH in Korean children and adolescents with ADHD; especially, the DRD4 VNTR polymorphism significantly interacted with both the ADRA2A DraI and the NET1 −3081(A/T) polymorphisms, whereas the DRD4 VNTR and the DAT1 VNTR polymorphisms showed trends of interaction with the ADRA2A MspI and the NET1 −3081(A/T) polymorphisms, respectively. We have also observed significant interactions within the noradrenergic neurotransmitter system; the ADRA2A DraI polymorphism interacted with both the NET1 G1287A and the NET1 −3081(A/T) polymorphisms, which seems to further complicate the MPH response in ADHD patients. On the other hand, no independent variable of genotypic polymorphism showed any significant association with MPH response, according to the results of our multivariate logistic regression.

Therefore, perhaps it could be argued that interactions among different polymorphisms might possibly exert stronger influence than any single polymorphism alone on response to MPH in ADHD patients, and this might have contributed to the aforementioned inconsistencies among former study results. And considering the many candidate genes suggested for ADHD (Banaschewski et al. 2010; Faraone and Mick 2010), no pharmacogenetic study of this disorder, including ours, has yet comprised enough genetic polymorphisms in the analysis simultaneously with their possible combinations of interaction. One of the first studies to examine the impact of multiple genes and their interactions on ADHD (Comings et al. 2000) involved as many as 42 genes. However, this study was not a pharmacogenetic one, although it should be acknowledged as a precursor of such studies including ours. Here, we have selected genes reportedly the most promising from each of the two major neurotransmitter systems implicated in MPH response, the dopaminergic and noradrenergic systems, and the consequent results support, with a favorable effect size for the moment, that genes involved in these two systems might interact to form important predictors of short-term response to MPH.

Among the genetic polymorphisms evaluated in this study, the pharmacogenetic role of the DAT1 VNTR polymorphism might have been the most inconsistent previously. Based on the former report, we had considered grouping the genotypes of this polymorphism into 9/9 versus others as well. However, too few 9-repeat alleles and 9/9 genotypes (10 and 2, respectively) were included in our sample. In any event, the differences in previous results are inclined to be attributed to multiple factors such as sample sizes, age and ethnic differences, inclusion and exclusion criteria, as well as response criteria, among others. Here, our response criterion comprised both the parent- and clinician-rated scales. In addition, the investigators had established high inter-rater reliability regarding the CGI-I. As parental ratings of treatment response, which were adopted in many previous studies, can be subjective and unreliable, the combined response criterion with the clinician-rated CGI-I scores might have improved the reliability of our data. Nonetheless, more objective outcome measurements, preferably involving endophenotypes of ADHD, are recommended for future studies.

The mechanisms of how genetic polymorphisms of the dopaminergic and noradrenergic systems interact on MPH response are highly elusive. According to our results, the DRD4 VNTR rather than DAT1 VNTR polymorphism seems to be more involved in these interactions and the former significantly interacted with polymorphisms of both the norepinephrine receptor and transporter genes. This is perhaps related to the fact that dopamine D4 receptor is known to have a high affinity for norepinephrine (Van Tol et al. 1991; Prince 2008), whereas dopamine transporter, of which the density is low in the prefrontal cortex, reportedly does not play a strong role in the dopamine neurotransmission in this brain region (Madras et al. 2005). And as MPH inhibits the reuptake of both dopamine and norepinephrine (Biederman and Spencer 1999; Prince 2008), the affinity between D4 receptor and norepinephrine might influence MPH response. Here, with our limited knowledge on the DRD4 VNTR polymorphism and its relationship to dopamine receptor function, it would be much too speculative to discuss a hypothesis for these genetic interactions, but whether the DRD4 VNTR polymorphism somehow modifies the affinity between D4 receptor and norepinephrine is perhaps worthy of future investigation.

From the norepinephrine perspective, norepinephrine transporter is probably an important contributor to dopamine neurotransmission in the prefrontal cortex (Madras et al. 2005), and this might at least partly contribute to the interaction between the DRD4 VNTR and the NET1 −3081(A/T) polymorphisms, although the mechanisms are unknown. However, the mechanisms underlying the significant interaction between the DRD4 VNTR and the ADRA2A DraI polymorphisms are even harder to determine. Some studies have observed in rats that α-2 adrenergic receptor agonists elevated frontal dopamine concentrations, whereas α-2 adrenergic receptor antagonists reduced frontal concentrations of dopamine (Gresch et al. 1995; Gobert et al. 1998). If the ADRA2A DraI polymorphism affects norepinephrine-induced frontal release of dopamine, this might help explain the observed interaction between the DRD4 VNTR and the ADRA2A DraI polymorphisms; however, further discussions are currently premature.

The results of this study also suggest that some clinical factors might influence MPH response as well. For example, severity of ADHD symptoms was found to be different at baseline, with the poor responder group showing a lower mean ADHD-RS score, which is a replication of previous findings in Korean children with ADHD (Cheon et al. 2005, 2007), and the poor responder group has also presented a higher comorbidity of oppositional defiant disorder (ODD) (i.e., 16.5% vs. 8.3%), although the difference was not statistically significant. In sum, a different spectrum of ADHD phenotypes with relatively less of ADHD and more of ODD symptomatology might be related to poor response to MPH. Whether these differences are related to the interaction of dopaminergic and noradrenergic gene polymorphisms is another interesting question for future studies.

Limitations

There are several limitations in this study. The present study was not a randomized, placebo-controlled design, and dose adjustments were also based on the clinician's judgment rather than a strict protocol. Absence of a control group implies that MPH responders were not distinguished from placebo responders. This would decrease the ability to detect true genetic effects and might increase type 1 errors. Although change of medication dose and clinical outcome of pharmacotherapy are both influenced by not only the therapeutic response but also the adverse effects of that drug, we did not systematically assess the side-effect profiles in this study. Accordingly, it is not clear whether the poor responders had insufficient treatment response to MPH or insufficient dosing because of lack of tolerability, even though the mean final MPH doses were comparable between the good and poor responders. In addition, weights were taken into account during the MPH titration process, but not systematically recorded, which adds another limitation. And we used the immediate- and sustained-release MPHs together, which have different dosage brackets; therefore, it was difficult for us to examine the gene–dose interactive effects as formerly investigated (McGough et al. 2009). In addition, different pharmacokinetic and pharmacodynamic profiles of these formulations could have influenced our results (Song et al. 2011). A possible strength of this study is that we have recruited an ethnically homogenous population and therefore increased the likelihood of detecting a genetic effect. However, it is also possible that the genetic effect detected here might be only true of this specific population and entail limited generalizability to other samples. Finally, our sample size was not large enough to analyze different subtypes of ADHD separately. In addition to the small total sample size, the good responder group was very small (n=24) and this should have negatively influenced the power of our analysis. Speaking of the overall poor MPH response in this study, this might be partly because we had used a more strict definition of response than usual, combining both the ADHD-RS and the CGI-I scores. Those who met only one of these two scores would have been assigned to the poor responder group. Another point of note is that the baseline ADHD-RS scores of the participants were relatively lower than those of other studies (Cheon et al. 2005, 2007; Song et al. 2011), although similar scores were also reported (Kim et al. 2006; Cho et al. 2008b). When the baseline score is low, it might be more difficult for the final score to attain a 50% reduction (for the ADHD-RS) or an apparent improvement (for the CGI-I) compared with the baseline. The reason for this favorable baseline score is not clear, but it probably should be mentioned as a limitation of this sample, along with the small total and responder group sizes. Given the small group sizes produced by dividing the sample into combined genotype as well as responder groups (see AppendixTable A1), these results are preliminary, and replication in a larger independent sample is warranted.

Conclusion

As ADHD is considered to be polygenic in nature, interaction analyses have been proposed to be useful in better understanding the genetic influences on this disorder (Gabriela et al. 2009). The results of the present study further suggest that the same argument is applicable to the study of the pharmacological treatment response in ADHD.

Clinical Significance

Overall, the results of this study suggest that genetic determinants of MPH response consist of both the dopaminergic and noradrenergic gene polymorphisms, and efforts to predict response to MPH should cover these two catecholaminergic systems and the multifaceted aspects of their interactions as well.

Footnotes

Disclosures

Drs. Hong, Kim, Cho, Shin, Kim, and Yoo have no conflicts of interest or financial ties to disclose.

Appendix

Table A2.

Possible Predictors of Response to MPH Treatment Based on an Alternative Response Definition ^a

Predictor	B	SE	β	p Value	95% CI
DAT1 VNTR	−0.971	0.991	−0.440	0.330	−2.940	0.998
DRD4 VNTR	−1.569	0.835	−0.873	0.064	−3.229	0.091
ADRA2A MspI	0.674	0.641	0.397	0.296	−0.600	1.947
ADRA2A DraI	−1.022	0.672	−0.564	0.132	−2.357	0.314
NET1 G1287A	−1.877	0.732	−1.106	0.012	−3.331	−0.423
NET1 −3081(A/T)	−0.548	0.663	−0.281	0.410	−1.865	0.769
DAT1 VNTR×DRD4 VNTR	0.585	0.637	0.345	0.362	−0.682	1.851
DAT1 VNTR×ADRA2A MspI	0.150	0.446	0.088	0.737	−0.736	1.036
DAT1 VNTR×ADRA2A DraI	−0.009	0.458	−0.005	0.984	−0.919	0.901
DAT1 VNTR×NET1 G1287A	1.259	0.558	0.720	0.026	0.150	2.367
DAT1 VNTR×NET1 −3081(A/T)	0.121	0.535	0.070	0.822	−0.942	1.184
DRD4 VNTR×ADRA2A MspI	−0.743	0.365	−0.419	0.045	−1.470	−0.017
DRD4 VNTR×ADRA2A DraI	0.761	0.383	0.446	0.050	−0.001	1.523
DRD4 VNTR×NET1 G1287A	0.782	0.396	0.414	0.052	−0.005	1.569
DRD4 VNTR×NET1 −3081(A/T)	0.534	0.409	0.315	0.195	−0.279	1.347
ADRA2A MspI×ADRA2A DraI	0.582	0.381	0.326	0.130	−0.175	1.338
ADRA2A MspI×NET1 G1287A	−0.564	0.375	−0.304	0.136	−1.309	0.181
ADRA2A MspI×NET1 −3081(A/T)	−0.691	0.374	−0.389	0.068	−1.434	0.052
ADRA2A DraI×NET1 G1287A	0.913	0.410	0.517	0.029	0.098	1.728
ADRA2A DraI×NET1 −3081(A/T)	−0.170	0.396	−0.100	0.670	−0.956	0.617
NET1 G1287A×NET1 −3081(A/T)	0.458	0.393	0.258	0.247	−0.322	1.238
Test			df		p Value
Goodness of fit	2.029		21, 89		0.012
R²	0.324
Adjusted R²	0.164

The Clinical Global Impressions-Improvement Scale score was used as the outcome variable that reflects the overall drug response.

ADRA2A=alpha-2A adrenergic receptor gene; B=unstandardized regression coefficient; CI=confidence interval; DAT1=dopamine transporter gene; df=degree of freedom; DRD4=dopamine D4 receptor gene; MPH=methylphenidate; NET1=norepinephrine transporter gene; β=standardized regression coefficient; SE=standard error; VNTR=variable number of tandem repeat.

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