The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization

doi:10.21203/rs.3.rs-4266434/v1

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The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization

https://doi.org/10.21203/rs.3.rs-4266434/v1

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Background Observational studies have showed that there was controversy over whether screen exposure could induce attention deficit hyperactivity disorder (ADHD). Therefore, a two-sample Mendelian randomization (MR) study was conducted to explore the potential genetic association between screen exposure time and ADHD.

Methods This study selected genetic variations in screen exposure time as instrumental variables (IVs) that included relevant genotype data of European populations from 437,887 cases time spent watching television (TSWT), 456,972 cases length of mobile phone use (LMPU), and 360,895 cases time spent using computer (TSUC), respectively. Simultaneously 20,183 cases European ADHD populations were selected as genome-wide association study data. The inverse variance weighted (IVW) method was used as the primary approach for analysis.
Results Research has shown that TSWT and LMPU have a positive and causal effect in increasing the risk of ADHD. According to the IVW analysis, the risk of ADHD with an odds ratio (OR) of 3.454631 [95% confidence interval (CI): 2.460256 - 4.850909], P = 8.17611E-13 in TSWT. The risk with OR of 2.0063796 (95%CI: 1.30737263 - 3.079121), P = 0.001440136 in LMPU. However, no causal effect of TSUC on ADHD was found in the analysis.
Conclusion The MR analysis provided evidence of the causal role of TSWT and LMPU in increasing the risk of ADHD. This suggests screen exposure might be a potential environmental risk factor for the development of ADHD.

Attention deficit hyperactivity disorder

Screen media exposure

Mendelian randomization

Causal relationship

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurological and psychiatric developmental disorder in children and adolescents. The prevalence of ADHD worldwide is about 7% (1.4%-3.0%)[1]. The core symptoms consist of impairing inattention, hyperactivity, impulsivity, and inappropriate development [2]. The etiology and pathogenesis of ADHD are not yet clear, and the interaction between environmental and genetic factors may lead to the occurrence of the disease. Epidemiological studies have identified the relationship between electronic products (such as television, mobile phones, and computers) and ADHD [3, 4]. Research has shown that early exposure to electronic devices may be associated with ADHD, with specific data ranging from a relative risk value of 1.1 to 1.5[5]. Screen exposure refers to the exposure and use of electronic products such as televisions, mobile phones and computers [6] and is considered one of the environmental risk factors leading to ADHD. A screen exposure cohort study of 152 newborns in Shanghai found that premature and prolonged screen exposure can affect their cognitive, language, and social development [7]. In addition, relevant studies have shown that premature exposure to electronic screen environments may increase the risk of developing ADHD [8]. With the rapid introduction of new technologies and rapid changes in media usage, the screen exposure time of children and adolescents is more than two hours per day [9, 10]. Therefore, previous studies have found a time-dependent correlation between playing games on mobile phones and ADHD [11, 12]. However, some studies have shown that there was no significant correlation between the daily use of mobile phones to answer and make calls and the incidence of inattention symptoms. The impact of mobile phone use on inattention symptoms may not directly come from the radio frequency electromagnetic radiation of mobile phones but from its impact on mental health. The previous study has found that lack of concentration was related to depression, anxiety, stress, etc [13, 14]. Due to the combined effects of heterogeneity in ADHD and sample size limitations, it was difficult to determine the causal relationship between screen exposure and ADHD through traditional observational studies. Therefore, in order to further explore the causal relationship between screen exposure time and ADHD, researchers should provide more reliable evidence.

Mendelian randomization (MR) is a randomized research design aimed at exploring the interactions between multiple gene loci, as well as the interactions between multiple gene loci and diseases. This method is based on the principle of instrumental variable analysis in statistics, using genes as instrumental variables for the exposure factors [15]. Due to the random allocation principle of alleles during gamete formation, the association between genes and diseases is not affected by confounding factors such as postnatal environmental and behavioral factors, and the causal time series is reasonable, making the estimated effect values closer to the actual situation. Compared with traditional randomized controlled trials, the MR method is more practical and convenient [16]. In this study, the two-sample MR analysis was used to evaluate the causal relationship between screen exposure time and ADHD.

The gene data of time spent watching television (TSWT), length of mobile phone use (LMPU), and time spent using computer (TSUC) publicly published in the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) in 2018 were used as a reference. According to the selection criteria of genetic instrumental variables (IVs), single nucleotide polymorphisms (SNPs) loci that were statistically significant and related to screening exposure time were selected as instrumental variables. The gene data related to ADHD were from the Psychiatric Genomics Consortium (PGC) published in 2017. Using SNPs screened through sensitivity analysis, different MR methods were used to determine the causal relationship between screen exposure time and ADHD. The study design and overview of the MR study was showen in Fig. 1.

2.1 Genome wide association study (GWAS) Summary Data for screen exposure time

Three sets of instrumental variables were used to describe screen exposure time, using data from the IEU Open GWAS database. The above data were all published by the MRC-IEU at the University of Bristol. TSWT included 437,887 cases with 113 SNPs (code: ukb-b-5192), LMPU included 456,972 cases with 31 SNPs (code: ukb-b-4094), and TSUC included 360895 subjects with 83 SNPs (code: ukb-b-4522).

2.2 GWAS Summary Data for ADHD

The data for ADHD was from the GWAS database, published by the PGC. A total of SNPs were screened from 20,183 cases in the ADHD group and 35,191 cases in the control group (code: ieu-a-1183).

2.3 Selection of IVs

MR analysis was used to reveal the potential impact of screen exposure on the development of ADHD. SNPs related to screen exposure were used as instrumental variables. SNPs closely related to exposure factors without linkage disequilibrium (LD) (r² < 0.001), within a 1.0×10⁴kb window, and meeting the whole genome significance level (P < 5 × 10^− 8) were selected. The strength of instrumental variables was evaluated through F-values (F = R²༏(1-R²) × [(N-K-1)/K]). Among them, R² represented the proportion of variation of IV explained by SNP, N represented the sample size, and K represented the number of SNPs. F > 10 represented that the possibility of weak instrumental variable bias was relatively low [17, 18].

2.4 MR analysis

The IVW method was used as the primary analysis method. The IVW method was a weighted linear regression model. All genetic variations are assumed to be effective instrumental variables [19]. The IVW method was used to weigh the estimated effects of each SNP on screen exposure and ADHD, and fixed effects or random effects models were used to summarize these estimates. The difference was statistically significant according to Bonferroni correction (P = 0.05/n_expose·n_outcome). In addition, other methods, including MR-Egger, weighted median, simple mode and weighted mode, were also employed.

2.5 Sensitivity analysis

To evaluate whether the test results violate the MR hypothesis, Cochran's Q test was used to detect heterogeneity, with IVW method and MR Egger regression as the main calculation methods. P < 0.05 indicated the presence of heterogeneity. A random effects model was used for MR analysis if heterogeneity existed [20]. MR Egger regression intercept test was used to test the level of pleiotropy. The intercept value and standard error were indicators for evaluating pleiotropy, and P < 0.05 indicates the existence of pleiotropy [21]. If the results of the MR Egger regression method indicate the presence of pleiotropy, pleiotropy residuals and outliers were used to evaluate and correct pleiotropy. The leave-on-one-out sensitivity test was conducted to determine whether SNPs affected the results of MR analysis.

The above statistical analyses were implemented using the TwoSampleMR package and MR-PRESSO package in R (4.3.3) language software.

In this study, the relationship between screen exposure time (TSWT, LMPU and TSUC) and ADHD was examined. Further calculations of OR (95% CI) and P-Value were performed for scenarios involving electronic devices and ADHD (Fig. 2).

3.1 MR of TSWT on ADHD

IVW and MR Egger tests were used to evaluate heterogeneity. The MR Egger test showed Q = 166.4992, P = 1.09E-05 and the IVW test showed Q = 167.0688, P = 1.29E-05, with P values less than 0.05, indicating the presence of heterogeneity in the study. The MR Egg intercept was 0.009398745, P = 0.5679151, indicating that there was no horizontal pleiotropy in this study. Therefore, the result of IVW was used to analyze the correlation between TSWT and ADHD. The IVW method showed a positive correlation between TSWT and ADHD (OR = 3.454631, CI: 2.460256–4.850909, P = 8.17611E-13 < 0. 0.00833). TSWT may be a relative risk of causing ADHD.

The sensitivity analysis using the "leave-one-method" showed that the included SNPs had no significant impact on the results of TSWT on ADHD. Regardless of whether any SNP was removed, the results of the remaining 98 SNPs were on the right side of the invalid line and were close to the interval of the total effect (β = 1.206160–1.299763, P < 0.01). The above result indicates that removing any included SNPs alone would not have a significant impact on the results, thus proving that the MR results of this study were reliable. (Fig. 3)

3.2 MR of LMPU on ADHD

The MR Egger test of LMPU on ADHD was Q = 60.09084, P = 0.000163046, and the IVW test was Q = 62.51108, P = 0.000122466. The P-values of the above tests were less than 0.05, indicating the presence of heterogeneity in the study. The MR Egg intercept analysis showed that there was no horizontal pleiotropy in the study (intercept = 0.02196794, P = 0.3155836). The result of random effects IVW was used to analyze the association between LMPU and ADHD. The IVW method showed a positive correlation between LMPU and ADHD (OR = 2.0063796 CI: 1.30737263-3.079121 P = 0.001440136 < 0.00833). LMPU might be a risk factor for ADHD.

The "leave-one-method" showed that the included SNPs had no significant impact on the results of LMPU on ADHD. Regardless of whether SNP was removed, the results of the remaining 29 SNPs were on the right side of the invalid line and were close to the interval of the total effect (β = 0.6275006–0.8143410, P < 0.01). The results indicates that the MR results of LMPU on ADHD were reliable. (Fig. 4)

3.3 MR of TSUC on ADHD

The MR Egger test was Q = 101.0478, P = 0.01106093, and the Inverse variance weighted test was Q = 101.6827, P = 0.01218073. Both P-values were greater than 0.05, indicating that there was no heterogeneity. The MR Egg intercept analysis indicated that there was no horizontal pleiotropy (intercept=-0.006991752, P = 0.5063527). Therefore, the results of IVW were used to analyze the association between TSUC and ADHD. The IVW method showed no correlation between TSUC and ADHD (OR = 0.741734, CI: 0.5354545–1.027481, P = 0.07234459 > 0.05).

The "leave-one-method" showed that the included SNPs had no significant impact on the results of TSUC on ADHD. Regardless of whether any SNP was removed, the results of the remaining 73 SNPs were on the left side of the invalid line and were close to the interval of the total effect (β = -0.3514026 - -0.2510942, P > 0.05). The result indicates that the MR results of LMPU on ADHD were reliable. (Fig. 5)

The results of this study indicated that screen exposure (television, mobile phones) had a statistically significant causal effect on the onset of ADHD. The conclusion of this study contributed to a further understanding of the etiology of ADHD, providing important references for developing prevention strategies for ADHD and identifying potential intervention measures.

Previous studies have shown that screen exposure may have an impact on the cognition, language, and social emotions of children and adolescents. The study shared that the time of watching television and using mobile phones had a risk of ADHD. A survey study from a longitudinal and cross-sectional in children (n = 596) [22] and a cross-sectional in young adults (n = 408) also confirmed this conclusion [23] that there was an association between early screen exposure and neurodevelopmental disorders. The previous study showed that individuals with ADHD began to be exposed to electronic screens earlier and for more extended periods, which easily affected their cognition development. Specifically, the ADHD population typically begins to be exposed to electronic screens during adolescence; they spend an average of 2 hours per day using electronic devices [24, 25]. Through a retrospective questionnaire survey, it was found that prolonged screen exposure increases the risk of ADHD [26]. Therefore, in order to promote the healthy development of cognitive and social abilities in children and young adults, the use of electronic devices by infants and young children should be limited.

An individual's genotype and phenotype had a causal relationship. However, there is a risk bias in the commonly used methods of estimating genotype-phenotype associations based on unrelated individuals. Factors such as population stratification and selection of sex can correlate between genotype and phenotype, which in turn affects the evaluation of causal relationships [27]. There was heterogeneity in the instrumental variables of TV viewing time and mobile phone usage, which may come from different analysis platforms, different experiments, or different populations. Therefore, future research should consider re-validating causal relationships through further single-sample MR methods in prospective ADHD cohorts.

Although the exact pathological mechanism between screen exposure and ADHD is not yet precise, some theories supply possible explanations. The "substitution hypothesis" suggests that screen exposure can attract children's attention, thereby replacing developmental learning opportunities. The present study found that the OR value of the incidence of attention deficit symptoms increases with the increased time of watching TV and using mobile phones. The results of a series of studies by Mortaza SM et al. that found a significant correlation between mobile phone use and attention deficit were consistent [8, 28]. Attention deficit is related to the abnormal structure and function of the prefrontal cortex circuit in the brain [29, 30]. Aalto et al. found that when mobile phone use was close to the human head, local blood flow near the prefrontal cortex of the brain was affected, which may be due to an increase in the incidence of attention deficit symptoms caused by mobile phone use [31]. The other potential reason was that the rapid parallel processing of visual information in screen exposure may hinder children's ability to think independently, leaving them with insufficient time to understand and reflect on the social context they encounter [32]. These theories were consistent with the results of this study, which suggested that early screen exposure might increase the risk of ADHD.

At the molecular level, more and more genes have been identified to be associated with ADHD. The forkhead box p2 (FOXP2) locus encodes a transcription factor expressed in the brain, closely related to the human ability to communicate through complex speech [33]. The FOXP2 gene discovered by GWAS was the only gene that overlaps with ADHD and telephone use [34]. The FOXP2 expression is related to sex hormone levels, which are related to various aspects of human speech. Due to the difference in FOXP2 expression, the incidence rate of male and female patients is different. Dopamine is considered the primary neurotransmitter involved in the pathophysiology of ADHD [35]. Strong evidence suggests that patients with ADHD have reduced dopamine metabolism, particularly related to impulsivity. However, playing video and computer games was observed to increase the release of dopamine in the striatum [36]. The random effects IVW method showed no correlation between time of using computers and ADHD in the study. The above results indicated that although some overlapping genes were found between screen exposure and ADHD, the role of genes in causing symptoms of ADHD was still controversial, which needs to be explored in the future.

Although this study has a large sample size and sufficient statistical power, the following shortcomings need to be considered: 1.The samples for this study were from European countries, so these conclusions might not be applicable to the people from other countries. 2. IVs were based on adult screen exposure choices, which could not be necessarily suitable for adolescents or children. 3. The impact of heterogeneity cannot be completely eliminated, which may be attributed to the complex and unclear biological functions of many genetic variations. 4. GWAS can provide new insights into ADHD related genes; further research needs to explore the molecular biology of ADHD.

In conclusion, the MR analysis implied that screen exposure time (TSWT and LMPU) and ADHD have a causal role. This suggests that the time of using electronics should be controlled to reduce the risk of ADHD. Furthermore, sensitive genes associated with ADHD may be a future study direction.

ADHD: attention deficit hyperactivity disorder; MR: Mendelian randomization; IVs: instrumental variables; TSWT: time spent watching television; LMPU: length of mobile phone use; IVW: inverse variance weighted; OR: odds ratio; CI: confidence interval; SNPs: single nucleotide polymorphisms; MRC-IEU: Medical Research Council Integrative Epidemiology Unit; PGC: psychiatric genomics consortium; GWAS: genome wide association study; LD: linkage disequilibrium; MAF: minor allele frequency; Forkhead box p2: FOXP2.

Ethics approval and consent to participate

This study did not require ethical approval and consent, as the research data were sourced from publicly available data compiled by GWAS.

Consent for publication

Not applicable

Availability of data and materials

All data used in this study are publicly available. To assess the data, please contact the corresponding author.

Competing interests

The authors declared no competing interests.

Funding

The present study was sponsored by Natural Science Foundation of Fujian Province by Fujian Science and Technology Department (Grant No: 2022J01414); Fujian Provincial Traditional Chinese Medicine Science and Technology Program (Grant No: 2021zyjc03).

Authors’ contributions

Qiong Fang participated in the study design, original-draft, methodology, data analysis and wrote the manuscript. Yuehao Cai, Jing Kang, and Yiyan Zhang participated in the investigation, methodology, and data analysis. Fubiao Ye contributed to design, supervision and revision. All authors have been involved in final version of the manuscript.

Acknowledgements

The data were provided by the MRC-IEU database and PGC databases. We would like to express our gratitude to the previous researchers who provided valuable gene summary statistical data for this study.

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No competing interests reported.

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The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization

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Abstract

Figures

1. Introduction

2. Methods

2.1 Genome wide association study (GWAS) Summary Data for screen exposure time

2.2 GWAS Summary Data for ADHD

2.3 Selection of IVs

2.4 MR analysis

2.5 Sensitivity analysis

3. Results

3.1 MR of TSWT on ADHD

3.2 MR of LMPU on ADHD

3.3 MR of TSUC on ADHD

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

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