Noradrenergic genes polymorphisms and response to methylphenidate in children with ADHD: A systematic review and meta-analysis.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, and methylphenidate (MPH) is considered one of the first-line medicine for ADHD. Unfortunately, this medication is only effective for some children with ADHD. This meta-analysis was conducted to evaluate whether noradrenergic gene polymorphisms impact the efficacy of MPH in children with ADHD.
METHODS: Candidate gene studies published in English until March 1, 2020, were identified through literature searches on PubMed, Web of Science, and Embase. Data were pooled from individual clinical trials considering MPH pharmacogenomics. According to the heterogeneity, the odds ratio and mean differences were calculated by applying fixed-effects or random-effects models.
RESULTS: This meta-analysis includes 15 studies and 1382 patients. Four polymorphisms of the NET gene (rs5569, rs28386840, rs2242446, rs3785143) and 2 polymorphisms of the α2A-adrenergic receptor gene (ADRA2A) gene (MspI and DraI) were selected for the analysis. In the pooled data from all studies, T allele carriers of the rs28386840 polymorphism were significantly more likely to respond to MPH (P < .001, ORTcarriers = 2.051, 95% confidence interval [CI]:1.316, 3.197) and showed a relationship with significantly greater hyperactive-impulsive symptoms improvement (P < .001, mean difference:1.70, 95% CI:0.24, 3.16). None of the ADRA2A polymorphisms correlated significantly with MPH response as a whole. However, G allele carriers of the MspI polymorphism showed a relationship with significantly inattention symptoms improvement (P < .001, mean difference:0.31, 95% CI: 0.15, 0.47).
CONCLUSION: Our meta-analysis results indicate that the noradrenergic gene polymorphisms may impact MPH response. The NET rs28386840 is linked to improved MPH response in ADHD children. And the ADRA2A MspI is associated with inattention symptom improvements. Further investigations with larger samples will be needed to confirm these results.Registration: PROSPERO (no. CRD42021265830).

Introduction

Attention-deficit hyperactivity disorder (ADHD) is a common childhood behavioral disorder. Systematic reviews indicated that the global community prevalence is between 2% and 7%, with an average of around 5%.[ Pharmacologic studies proved that catecholamines dopamine (DA) and norepinephrine (NE) are involved in the disorder.

Methylphenidate (MPH) is the most commonly used drug among psychostimulants for the treatment of ADHD. It blocks the DA and NE transporter and causes the synaptic concentration of these neurotransmitters to increase.[ Unfortunately, the clinical response varies significantly between patients. A considerable proportion of patients (approximately one-third) do not respond adequately to stimulant treatment or poor tolerance.[ The main reason is that genetic factors may contribute to individual differences of the efficacy of drug therapy.[ Several studies have been conducted to examine the association between genetic factors and MPH treatment response. Drug target-related pharmacodynamic genes, especially the dopamine system genes and noradrenergic system genes, have been proved to be associated with the efficacy of MPH medication. Among them, noradrenergic genes like norepinephrine transporter gene (NET/SLC2A6) and α2A-adrenergic receptor gene (ADRA2A) are pivotal factors that affect the treatment effect of MPH.[

Norepinephrine transporter protein (NET) is encoded by NET and is responsible for NE and DA reuptake and maintains the norepinephrine-dopamine balance in the frontal lobe.[ The sensitivity of NET to MPH was similar to that of DA transporter.[ Evidence suggests that NET gene polymorphism is a candidate gene for MPH treatment response, among which the silent polymorphism rs5569(A>G) and functional promoter variant rs28386840(T>A) are examined most frequently.[ In addition, other promoter SNPs such as rs2242446(C>T) and rs3785143(C>T), which regulate the expression of NET, have also been reported to be associated with ADHD in candidate gene studies and have been investigated in pharmacogenetic studies.[

ADRA2A encodes the α2A-adrenergic receptor. The activation of this receptor enhances the function of the prefrontal cortex, which is a crucial area of deficits in ADHD, including working memory, focused attention, and response control.[ Besides, stimulation of the α2 receptor mediates the increase in intrinsic excitability induced by MPH.[ Therefore, the ADRA2A gene is also regarded as one of the candidate pharmacogenomics genes of MPH response, with rs1800544(G>A,C) in the MspI site and rs553668(A>G,T) in the DraI site, located in the 5’-promoter region and the 3’-non-coding region, respectively, becoming genetic polymorphisms of interest.[ In addition to the NET and ADRA2A genes, other noradrenergic system genes such as the Monoamine Oxidase A gene (MAOA) and dopamine β-hydroxylase (DBH) also play an essential role in the etiology of the disease, but the pharmacological research of MPH is still lacking.[

However, results from limited studies on the role of NET and ADRA2A genes in MPH response have been inconsistent.[ A genome-wide association study (GWAS) of response to MPH in ADHD children found that the rs17841329 and rs192303 polymorphisms of the NET gene were found to be associated with treatment outcomes.[ Nevertheless, in a recent genome-wide association study, no associations were reported.[ Recently, a meta-analysis of the relationship between MPH response and 6 candidate genes was conducted on the pharmacogenomics study of children with ADHD. It was found that MspI in the ADRA2A gene, rs5569 and rs28386840 in the NET gene were associated with improved MPH response,[ although it was not replicated.[

Taking into account the importance of norepinephrine in the activation of MPH medication as well as the inconsistent findings of the noradrenergic gene polymorphisms in terms of treatment effects. This meta-analysis involved the latest studies and added 3 SNPs (DraI, rs2242446, and rs3785143) of noradrenergic genes that were not investigated in the previous meta-analysis. In order to comprehensively and accurately evaluate the effects of noradrenergic genes on the treatment outcome, we further included quantitative measures like changes in behavioral symptoms and neurocognitive function as outcome variables. As the outcomes may vary due to the use of different assessment tools and various aspects of the evaluator and duration of treatment, a subgroup analysis was conducted to assess this influence. Therefore, the aims of this study are to identify the association between noradrenergic genes (NET and ADRA2A) and MPH response in children with ADHD by using outcome variables of dichotomous measures and quantitative measures, including behavioral symptoms and neurocognitive functions; add polymorphisms rs2242446 and rs3785143 in NET and DraI polymorphism in ADRA2A; examine the effects of other factors that may influence the relationship between noradrenergic gene and MPH response in children with ADHD, such as drug dosage, treatment duration, comorbidity, gender, ADHD subtype, treatment duration, and evaluation tools.

Methods

The study was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses protocols (PRISMA-P) statement.[ The protocol of the systematic review has been registered in PROSPERO (registration number CRD42021265830).

Search strategy

We conducted a literature search using PubMed, Web of Science, and Embase to identify articles in English published before March 1, 2020. The search queries used were combinations of the following phrases: “Attention-deficit hyperactivity disorder or ADHD”, “NET or SLC6A2 or Norepinephrine Transporter”, “ADRA2A or Alpha-2A adrenergic receptor or α2 receptor”, “noradrenergic or norepinephrine”, “methylphenidate or MPH”, “response or efficacy”, “gene or polymorphism”. (see Table S1, Supplemental Digital Content for detailed search strategy).

Inclusion criteria, data extraction, and outcome

The following inclusion criteria were applied to select studies that can be included: if the patient cohort included children and/or adolescents under 18 years of age; articles on the relationship between NET or ADRA2A polymorphisms and MPH clinical efficacy; the diagnosis of ADHD must be determined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R, DSM-IV, and DSM-IV-TR) or the International Classification of Diseases (ICD-10); genotype distribution was in Hardy-Weinberg equilibrium; studies adopted the candidate gene approach; studies was published in a peer-reviewed journal in English.

Studies were excluded if their results did not include quantitative or dichotomous measures that can be transformed into odds ratio (OR) or mean difference (MD). Two authors (D-F.Y. and M-X.Z.) independently extracted and checked the data. Any disagreement was resolved through discussion until a consensus was reached with the third reviewer (Y.H.). The efficacy of MPH was evaluated from dichotomous outcomes and continuous outcomes. The MPH response was regarded as an indicator of the dichotomous outcomes and evaluated by ADHD rating scale (ARS) or clinical global impression-improvement scale (CGI-I). If multiple data sources existed for categorical analysis (ARS and CGI-I), we preferred to pool data from ADHD-RS and CGI-I or CGI-I. The changes in behavioral symptoms of ADHD and neurocognitive function were considered as the continuous outcomes and pooled from ARS-subscore, Swanson, Nolan, and Pelham-IV (SNAP-IV) rating scale, and Continuous Performance Test (CPT). All conducted tests were 2-tailed, and P values less than.05 were considered significant.

Quality assessment

The selected studies’ quality was assessed using the Methodological Index of Non-randomized Studies (MINOR).[ The outcomes of the MINOR ratings are shown in Table 1. The global ideal score is 16 for non-comparative studies and 24 for comparative studies. The items are scored: 0 (not reported), 1 (reported but inadequate), or 2 (reported and adequate). The quality of each study was independently evaluated by 2 authors (D-F.Y. and M-X.Z.), and dispute cases were resolved through consensus with the third reviewer (Y.H.).

Table 1

Description of included studies.

Study	Sample size	Country	Age	Male (%)	ARS baseline	Subtype (I/H/C%)	Comorbidity (%)	Diagnostic criteria	Duration (wk)	Drug dose	Outcome measure	SNP	Quality score
Kim 2010	112	Korea	9.2	88.5	26.9	7.8	ODD:13.4 anxiety:1.07	DSM-IV	8	29.2 mg/d	CGI-I	rs28386840 rs5569	11
Hong 2012	103	Korea	9.1	NA	26.9	26.9	ODD:14.6, anxiety:11.7	DSM-IV	8	29.1 mg/d	CGI-I and ARS	rs28386840 rs5569,MspI,DraI	11
Song 2011	114	Korea	9.08	83.3	32.2	9	NA	DSM-IV	8	29.47 mg/d	CGI-S or ARS	rs5569	9
Gough 2009	82	America	9.82	77	NA	14.8	ODD/OD:41.5 anxiety:5	DSM-IV	4–5	0.3-2.4 mg/kg/d	ARS	rs5569	9
Yang 2004	45	China	10.08	77.8	48.8	NA	NA	DSM-IV	NA	0.45-0.6  mg/kg/d	ARS	rs5569	8
Lee 2011	112	Korea	10.2	83	31.1	23	ODD/OD:31.25 anxiety:7.14	DSM-IV	8	0.85 mg/kg/d	CGI-I and ARS	rs5569,rs2242446	10
Park 2012	53	Korea	9.06	84.9	28.3	13	ODD/OD:10.8, anxiety:5.4	DSM-IV	8	0.86 mg/kg/d	ARS and CPT	rs5569,rs28386840	11
Unal 2016	108	Turkey	9.9	72.7	NA	0	ODD/OD:42.9	DSM-IV	4–6	0.7-1.1 mg/kg/d	CGI-S,CPRS/CTRS, GAS	MspI	10
Huang 2017	59	China	11.47	89.9	NA	NA	NA	DSM-IV	4	0.9 mg/kg/d	SNAP-IV	MspI	9
Park 2013	115	Korea	9.1	81.7	NA	16.4	ODD:16, anxiety:8	DSM-IV	8	0.81-0.83 mg/kg/d	CGI-I and CPT	Mspl,Dral	9
Cheon 2009	114	Korea	9.08	83.3	32.2	9	ODD/OD:3.5 anxiety:10.5	DSM-IV	8	29.5 mg/d	CGI-I and ARS	Mspl	11
Angyal 2018	122	Korea	9.3	88.5	32.9	NA	NA	DSM-IV	20-24	0.55 mg/kg/d	CGI-S and ARS	rs28386840,rs5569 rs2242446	10
Polancyk 2007	106	Brazil	10	77.4	NA	7.5	ODD/OD:67.9, anxiety:23.6	DSM-IV	4	0.65 mg/kg/d	SNAP-IV	Mspl	11
da Silva 2008	59	Brazil	12	76.3	NA	NA	ODD/OD:40.7, anxiety:44	DSM-IV	4	0.63 mg/kg/d	SNAP-IV	Mspl	10
Kim 2015	78	Korea	9.6	79.5	25.9	77.5	ODD:5.5	DSM-IV	8	0.67 mg/kg/d	CGI-I	rs28386840,rs5569 MspI, DraI	11

ARS = ADHD rating scale, CD = conduct disorder, CGI-I = clinical global impression of improvement, CGI-S = clinical global impression of severity, CPT = continuous performance test, DSM-IV = diagnostic criteria of mental disorder IV, I/H/C = inattentive/hyperactive/combined subtype, MPH = methylphenidate, NA = not available, ODD = oppositional defiant disorder, SD = standard deviation, SNAP-IV = Swanson, Nolan, and Pelham Scale version IV, SNP = single nucleotide polymorphism.

Ethical approval

All data in this meta-analysis were extracted from the previous published studies, no ethical approval or patient consent was required.

Data analysis

The heterogeneity between studies was examined by Cochran Q and I tests.[ Data were analyzed using a random-effects model (if I ≧ 50%) or a fixed-effects model (if I ≦ 50%).[ For P values less than.05, the heterogeneity was considered statistically significant. The MD and its 95% confidence interval (CI) were calculated for continuous outcomes. The OR and its 95% CI were calculated for dichotomous outcomes. The results may vary due to the use of different evaluation tools and the aspects of the evaluators. And the intervention time may also influence the effects of gene polymorphisms. We performed a subgroup analysis based on the length of treatment duration and different evaluation tools. Furthermore, in order to assess the influence of potential confounding variables, we performed a meta-regression using age, sex (%), subtype (% inattentive/hyperactive/combined subtype), comorbidity (% oppositional defiant disorder and conduct disorder), treatment duration (weeks), the dose of MPH, study quality, and baseline severity of ADHD. Publication bias was visually assessed by conducting funnel plots of symmetry. Data were analyzed using Comprehensive Meta-Analysis 2.0 (Biostat Inc., Englewood, NJ). And Stata/IC 12.0 (Stata Corporation, College Station, TX) was used to perform the meta-regression and sensitivity analyses. Publication bias analysis was performed if there were more than 5 studies.[

For each statistically significant association identified, we estimated the false-positive report probability (FPRP).[ The FPRP value was determined by the P value, the prior probability for the association, and statistical power. We calculated FPRP assuming a prior probability of.1 proposed for candidate gene analyses.[ The statistical power was based on the ability to detect an OR of 1.50, with α equalled to the observed P value.[ To assess whether the association was noteworthy, we set the FPRP cut-off value to.2 and advocated for summary analyses. Hence, an FPRP value < . 2 was considered to indicate a robust association.[

Results

Included studies

The flow chart of study selection and inclusion is shown in Figure 1. The characteristics of the included studies are listed in Table 1. Tables 2 and 3 show the analysis of dichotomous data and continuous data, respectively. In all studies, the authors mentioned that the genotype distributions of NET and ADRA2A polymorphisms are in Hardy-Weinberg equilibrium (HWE).

Figure 1

Flowchart of the study's inclusion and exclusion criteria.

Table 2

Association between the NET and ADRA2A gene polymorphisms and methylphenidate response (Dichotomized).

		Test for overall effect				Heterogeneity
Polymorphism	N	OR	95% CI	P	FPRP	Z	P	I2 (%)
rs5569
GG vs A carriers	9	1.716	1.049, 2.806	.03∗	0.488	2.152	.09	60.569
G carriers vs AA	5	1.448	0.740, 2.834	.28	0.823	1.081	.176	36.811
GG vs AA	4	0.921	–0.208, 0.835	.84	0.908	–0.208	.712	0.000
GG vs GA	4	1.280	0.563, 2.909	.56	0.885	0.589	.008	74.679
Assessed by CGI	7	1.501	1.087, 2.071	.12	0.378	2.470	.009	65.106
Assessed by ARS	3	2.919	1.628, 5.235	.00	0.321	3.594	.607	0.000
rs28386840
T carriers vs AA	5	2.051	1.316, 3.197	.002∗∗	0.140	3.172	.566	0.000
TT vs A carriers	2	1.120	0.530, 2.340	.77	0.898	0.290	.180	43.000
MspI
GG vs C carriers	6	0.865	0.393, 1.904	.72	0.897	–0.361	.001	74.762
GG vs CC	3	0.693	0.072, 6.709	.76	0.929	–0.317	.009	78.917
GC vs CC	3	1.098	0.543, 2.220	.80	0.899	0.259	.726	0.000
G carriers vs CC	3	1.095	0.568, 2.109	.79	0.895	0.271	.262	25.263
>7 wk	4	1.297	0.562, 2.990	.54	0.885	0.609	.009	74.311
≤4 wk	2	0.303	0.118, 0.778	.01	0.699	–2.484	.841	0.000
DraI
CC vs T carriers	2	1.34	0.770, 2.340	.30	0.865	1.040	.750	0.000
rs2242446
TT vs C carriers	2	0.730	0.410, 1.290	.28	0.801	1.090	.340	0.000
rs3785143
CC vs T carriers	2	1.280	0.710, 2.310	.41	0.841	0.820	.550	0.000

ARS = ADHD rating scale, CGI-I = clinical global impression, CI = confidence interval, FPRP = false positive reporting probability, N = number, OR = odds ratio.

P < .05.

P < .01.

Table 3

Association between the NET and ADRA2A gene polymorphisms and symptom improvement (Continuous).

		Test for overall effect			Heterogeneity
Polymorphism	N	MD	95% CI	P	Z	P	I2 (%)
rs28386840 T carriers VS AA
IA	2	0.84	0.44, 2.18	.19	1.3	.75	0
Hy/Imp	2	1.70	0.24, 3.16	.02∗∗	2.34	.48	0
CE	1	–19.48	–32.09, –6.88	.003∗∗
rs5569 GG vs A carriers
Hy/Imp	2	2.80	–2.18, 7.78	.27	1.1	.11	60
OE	1	–15.19	–25.81, –4.57	.006∗∗
MspI G carriers vs CC
IA	3	0.31	0.15, 0.47	.0002∗∗	3.79	.77	0
MspI GG vs C carriers
RTSD	2	–6.38	–7.8, –4.96	<.00001∗∗	8.82	.56	0
RT	2	–7.91	–9.50, –6.31	<.00001∗∗	9.7	.65	0
DraI
CE	2	–10.83	–12.69, –8.97	<.00001∗∗	11.39	.36	0
RTSD	2	–5.62	–12.81, 1.57	.13	1.53	.04	76

CE = commission errors, Hy/Imp = hyperactive/impulsive, IA = inattention, OE = omission errors, RT = response time, RTSD = response time variability.

P < .05.

P < .01.

Relationship between NET rs28386840 polymorphism and MPH response

Figure 2 demonstrates the forest plot of the relationship between the NET rs28386840 polymorphism and MPH response using different genetic contrasted models. The pooled analysis from 5 studies[ showed that T allele carriers were significantly associated with a better MPH response (P < .001, ORTcarriers = 2.051, 95% CI:1.316, 3.197). Two studies[ further evaluated the behavioral symptom reduction of ADHD and observed a correlation was between the rs28386840 polymorphism and the hyperactive-impulsive score (Table 3). Using the ARS, we found that the T allele carriers were associated with more hyperactive-impulsive improvement (P = .02, MD Tcarriers = 1.70, 95% CI:0.24, 3.16).

Figure 2

The forest plot for the association between NET rs5569 polymorphism and MPH response using different genetic contrasted models.

Relationship between NET rs5569 polymorphism and MPH response

Figure 3 shows the forest plot for the relationship between the NET rs5569 polymorphism and MPH response using different genetic contrasted models. The pooled OR from 9 studies showed that the GG genotype of rs5569 was associated with a better response to MPH in children than A allele carriers (P = .03, ORGG = 1.716, 95% CI:1.049, 2.806). As for outcome measures, 3 of the selected studies[ used the ADHD Symptom Rating Scales (ARS), and 7 of the studies[ used the CGI-I. Subgroup analysis indicated that the differences in outcome measurement tools (ARS and CGI-I) contributed to the effect of heterogeneity on MPH response (Table 2). A meta-regression analysis to study the effect of covariates on heterogeneity showed that age and MPH dose might contribute to heterogeneous effects of MPH response (Table 4).

Figure 3

The forest plot for the association between NET rs28386840 polymorphism and MPH response using different genetic contrasted models.

Table 4

Results of meta-regression analysis.

		Fixed effect regression
	Polymorphism	Point estimate lower limit	Upper limit	P value
rs5569 GG vs C carriers
Age	–1.002	–1.652	–0.351	.003∗∗
Gender	–0.069	–0.152	0.014	.103
Subtype	–0.008	–0.024	0.007	.290
Drug dose	1.400	–0.040	2.860
ARS baseline	0.023	–0.031	0.077	.411
ODD/CD	–0.024	–0.059	0.011	.176
Anxiety	–0.005	–0.116	0.107	.935
rs28386840 T carriers vs AA
Age	–1.443	–3.827	0.741	.236
Subtype	–0.01	–0.027	0.007	.255
Drug dose	0.690	–3.730	5.120
Gender	0.072	–0.051	0.196	.251
ODD/CD	0.001	–0.016	0.019	.883
ARS baseline	0.013	–0.146	0.171	.877
MspI G carriers vs CC
Age	–0.82	–1.361	–0.286	.002∗∗
Drug dose	2.170	–3.460	8.600
Gender	0.045	–0.059	0.150	.396
Subtype	–0.017	–0.032	–0.0008	.040∗
ODD/CD	–0.048	–0.088	–0.008	.019∗

ARS = ADHD rating scale, CD = conduct disorder, ODD = oppositional defiant disorder.

P < .05.

P < .01.

Relationship of ADRA2A Mspl and DraI polymorphism to MPH response

Figure 4 illustrates the main results of the meta-analysis of the association between ADRA2A variants and MPH response. Our pooled analysis revealed no significant association between ADRA2A Mspl and and MPH response under different inheritance patterns. However, based on evidence from 3 studies,[ a correlation was observed between the MspI site and the reduction in the inattentive score. The presence of the G allele was associated with elevated inattentive symptom improvement to MPH in ADHD children (P < .001, MDG carriers: 0.31, 95% CI: 0.15, 0.47). Two studies[ further investigated the changes in neurocognitive function and found that GG genotype was related to more improvement in the response time variability (P < .001 MDGG: –7.91, 95% CI: –9.50, –6.31) (Table 3). As for the duration of treatment, 2 selected studies[ used MPH for 4 weeks, and 4 studied[ in 8 weeks. Subgroup analysis showed that the treatment duration could not explain the heterogeneity of effect on MPH response (Table 2). In the meta-regression analysis, age, comorbidity of oppositional defiant disorder/conduct disorder (ODD/CD), and subtype in ADHD might contribute to the heterogeneity in the effect estimates (Table 4). Three of the selected studies[ investigated the relationship between ADRA2A DraI SNP and MPH response. Our results showed no significant association for MPH response with ADRA2A DraI polymorphism (P = .30, ORCC = 1.34, 95% CI:0.77, 2.34) (Table 2).

Figure 4

The forest plot for the association between ADRA2A MspI polymorphism and MPH response using different genetic contrasted models.

Relationship of NET rs2242446 and rs3785143 polymorphism and MPH response

Two studies[ investigated the rs2242446 polymorphism and found no significant association (P = .28, ORTT = 0.73, 95% CI:0.41, 1.29). Another 2 studies[ investigated the rs3785143 polymorphism and observed no significant association between the rs3785143 genotype and MPH response (P = .41, ORCC = 1.28, 95% CI:0.71, 2.31) (Table 2).

Sensitivity analysis and publication bias

Sensitivity analysis was performed by sequentially omitting individual studies. The pooled results were stable in the studies of rs28386840 and MspI polymorphisms, indicating that the significance of pooled OR was not excessively influenced by any single study. However, when the study of rs5569 polymorphism was removed, the pooled result was not significant anymore (Table S1, Supplemental Digital Content). We assessed the publication bias by visually inspecting the funnel chart. The funnel chart was symmetrical, indicating that the publication bias was acceptable (Fig. 5).

Figure 5

A, Funnel plot indicating publication bias of included studies comparing MPH efficacy of rs5569 polymorphism. B, Funnel plot indicating publication bias of included studies comparing MPH efficacy of MspI.

FPRP analyses

Table 2 represents the calculated FPRP values for the main significant findings in this meta-analysis. Assuming that the prior probability was.1, the FPRP value of TT+AT on rs28386840 compared with the AA genotype was less than.2, indicating a significant association.

Discussion

In this meta-analysis, 15 studies and 1382 patients were evaluated to assess the effect of noradrenergic genes on the treatment response of MPH in children with ADHD. The meta-analysis found that the T allele of rs28386840 in the NET gene was associated with improved treatment effects in both MPH response and hyperactive symptom change, and the G carriers at MspI locus might be associated with greater improvement in the inattention symptoms. Through meta-regression analysis, we further found that the comorbidity of ODD/CD, age, medicine dosage, and subtype may influence the study results. To the best of our knowledge, this study is the latest and comprehensive meta-analysis to investigate the relationship between noradrenergic gene polymorphism and methylphenidate response.

The analysis of the rs28386840 of NET indicated that the T allele carriers might have a better response to MPH. Our findings confirmed the results of a previous meta-analysis of pharmacogenetic predictors of methylphenidate efficacy in children with ADHD and found a link between the T allele and improved MPH response.[ Due to the sequence alteration of the repressor binding site, the rs28386840 in the upstream promoter region has a major influence on the expression level of NET.[ The T allele is associated with a significantly down-regulated promoter function, which will result in a decrease in the NET expression.[ As the NET blocking efficiency of MPH may be more significant when the transporter level is low, the T carriers of this SNP might induce a good response of the stimulants. On the contrary, the A allele is associated with up-regulated promoter function. Thus homozygous carriers of the A allele are associated with a higher rate of ADHD.[ Furthermore, our results suggested that AT or TT genotypes displayed more reduction in hyperactive-impulsive symptoms after the treatment with MPH. This was supported by the results of the neuropsychological study that the AT+TT genotype showed more improvement in the mean commission error scores (a measure of impulsivity).[ Together with our results, rs28386840 of NET might be an important genetic marker for predicting MPH efficacy.

Rs5569 in the exon region of the NET gene has not been proven to be associated with ADHD in previous studies. However, this SNP is located in a haplotype block associated with atomoxetine, which is another drug treatment of ADHD and a specific selective norepinephrine reuptake inhibitor. Since MPH is a selective norepinephrine and dopamine reuptake inhibitor, it may also play a role in the pharmacogenetic effect of MPH.[ In Oh Young's study, the establishment of rs5569 seems to indicate that the G allele may have a protective effect on the development of ADHD symptoms.[ In this study, we found that GG individuals of rs5569 were more likely to have better responses to MPH than the C carriers, which is consistent with the previous study.[ Nevertheless, Angyal et al[ has provided the opposite result on the role of rs5569 in MPH response. Moreover, after conducting the sensitivity analysis, we discovered that the association between rs5569 and MPH response was primarily influenced by the result of Song et al,[ which means that this association result with MPH response was relatively unstable. Whether this polymorphism has effects on MPH response needs to be further investigated in the future with a larger sample.

MspI polymorphism in the promoter region is one of the most important polymorphisms among ADRA2A gene and plays a major role in gene expression and regulating the neurotransmitter release. In this study, we did not find any significant association of this polymorphism with MPH response as a whole. This is inconsistent with the previous meta-analysis of ADRA2A polymorphisms and MPH responses, indicating that the G allele of MspI is associated with improved responses.[ Although some previous studies reported that the presence of the G allele in the ADRA2A MspI polymorphism was associated with the improvement of MPH response, more recent studies have reported that no significant association of MspI polymorphism with MPH response have been found. In addition, some studies have found a correlation between the MspI polymorphism and the treatment effects of MPH in inattention symptoms or the inattention subtype, suggesting that this polymorphism may specifically influence MPH on inattention symptoms. In fact, after we further utilized quantitative outcome measures, we did find that G allele carriers of the MspI polymorphism of ADRA2A were related to significant inattention symptoms improvement but had nothing to do with MPH response as a whole. One of the reasons is that, compared with quantitative measures, dichotomous measures of MPH outcome may be less capable of detecting effects.[ Our results indicate that after MPH treatment, patients with the G allele showed a greater reduction in inattention symptoms.

This analysis could not find any relationship between MPH response and DraI, which is another important genetic polymorphism in the promoter region of the ADRA2A gene besides MspI. Similarly, the rs2242446 polymorphism, which is in high linkage disequilibrium (LD) with rs28386840 and rs3785143 that might impact the transcriptional activity and expression of NET has not been found to have a significant association with MPH response.[ The potential reason for the negative outcomes was that the sample size and the number of studies were relatively small. Future research should be conducted in larger samples to elucidate the role of these SNPs and the treatment effects of MPH.

The present meta-regression analysis suggested that MPH dosage is an important covariate leading to the heterogeneity of the treatment efficacy. The previous study has reported that MPH dose might influence the efficacy outcome.[ Our results show that the association between noradrenergic genes and MPH response is stronger in studies with relatively large drug doses. In the future, more studies are needed to clarify the gene × dose interactive effects. Besides, the association between genetic variants and MPH response is relatively strong in younger patients, which is consistent with the previous meta-analysis that assessed the association between rs5569 of NET and MPH response.[ In addition, the comorbidity of ODD/CD in ADHD might contribute to the heterogeneity of MPH response. The higher ODD/CD comorbidity rate may reduce MPH response and increase disease severity, which is in line with the previous studies that coexisting behavioral problems were negative predictors of treatment response.[ Furthermore, we found that the ADHD subtype is a covariate that contributes to the heterogeneity of the treatment efficacy. This can be proved by previous trials that noradrenergic gene polymorphisms may have different effects on different dimensions of ADHD symptoms during MPH treatment.

The subgroup analysis revealed that variation among outcome measures tools (ARS or CGI-I) might impact the therapeutic response of MPH. However, no effect was found on the duration of treatment (4 weeks or 8 weeks). Future studies should consider these factors to facilitate the evaluation of the impacts of MPH response.

Although many studies predicted the therapeutic response of MPH, little work has been done to test the predictors of MPH side effects. According to the pharmacodynamic gene study of MPH on the side effects of ADHD, the ADRA2A gene polymorphisms are associated with the increase of diastolic blood pressure (DBP) after therapy. The NET gene polymorphisms are associated with increased heart rate changes, increased frequency of irritability, increased severity of talking less, and disinterest symptoms (Table 5).

Table 5

Association of noradrenergic genes polymorphisms and side effects of methylphenidate.

Gene	Polymorphism (s)	Authors	Sample	Findings
ADRA2A	DraI	Yoo et al 2020	N = 83	Sleep side effects
	MspI	Cho et al 2012	N = 101	More change in diastolic blood pressure (DBP) associated with MspI G allele
NET	A-3081T	Yoo et al 2020	N = 83	Sleep side effects
	A-3081T	Cho et al 2012	N = 101	More increase in heart rate associated with TT genotype at rs28386840
	rs192303	Song et al 2014	N = 83	Increased frequency of irritability associated with rs28386840 G allele increased severity of talk less and disinterest symptom associated with CC genotype at rs192303
	rs3785143	Song et al 2014	N = 83	Increased frequency of irritability associated with CC genotype at rs3785143 increased severity of talk less and disinterest symptom associated with rs192303 T allele

This meta-analysis has several limitations. First, the drug response includes not only efficiency but also side effects. Most pharmacogenetic studies only focused on efficiency, and few studies focused on the potential adverse events of the noradrenergic genes, such as cardiovascular side effects, irritability, and disinterest.[ Due to limited research on pharmacogenetic side effects of MPH, more research should be conducted to assess the potential role of noradrenergic genes in side effects. Secondly, the studies included in our analysis are varied in terms of outcome measures and thresholds for significant improvement, especially the inconsistent standard in ADHD rating scale (ARS) and CGI-I for assessing the treatment response. However, we conducted subgroup analysis to reduce the potential impact of the different outcome measures contributing to the heterogeneity. Finally, the included studies and sample size of this analysis were relatively small and the observing treatment time was relatively short. Therefore, more studies especially the genome-wide association study should be enrolled in to improve the effect size.

Conclusion

In conclusion, this meta-analysis indicates that T carriers of rs28386840 in the NET gene are associated with improved MPH treatment. Patients with G carriers of MspI may have a greater improvement in inattention symptoms. The results of this meta-analysis may provide additional pharmacogenetic evidence for the treatment response of MPH in children with ADHD and further develop a personalized treatment plan for ADHD patients. Larger sample size and longer treatment duration should be needed to clarify the role of noradrenergic gene polymorphisms and MPH response of children with ADHD.

Acknowledgments

We wish to express our great appreciation to all the authors of the studies included in the current meta-analysis.

Author contributions

Y.H. designed the study. D-F.Y. and M-X.Z. were responsible for the collection of data and performing the statistical analysis and manuscript preparation. X-W.W. and J.J. were responsible for checking the data. All authors were responsible for drafting the manuscript, read and approved the final version.

Conceptualization: Danfeng Yuan.

Data curation: Danfeng Yuan, Jian Jiao.

Investigation: Manxue Zhang, Jian Jiao.

Methodology: Manxue Zhang, Yan Huang.

Project administration: Manxue Zhang.

Resources: Yan Huang.

Software: Yan Huang, Xinwei Wang.

Supervision: Xinwei Wang, Yi Huang.

Validation: Yi Huang.

Visualization: Yan Huang.

Writing – original draft: Danfeng Yuan.

Writing – review & editing: Yi Huang.