{"paper_id":"4d3ebcea-1d36-4649-b7f2-e74d65c3200e","body_text":"The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization Qiong Fang, Yuehao Cai, Jing Kang, Yiyan Zhang, Fubiao Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4266434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction 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. 2. Methods 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) (r 2 < 0.001), within a 1.0×10 4 kb 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 = R 2 ༏(1-R 2 ) × [(N-K-1)/K]). Among them, R 2 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. 3. Results 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 ) 4. Discussion 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. 5. Conclusion 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. Abbreviations 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. Declarations 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. References Salari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, Akbari H, Mohammadi M: The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis . ITAL J PEDIATR 2023, 49 (1):48. Rajaprakash M, Leppert ML: Attention-Deficit/Hyperactivity Disorder . PEDIATR REV 2022, 43 (3):135-147. Soares P, de Oliveira PD, Wehrmeister FC, Menezes A, Goncalves H: Is Screen Time Throughout Adolescence Related to ADHD? Findings from 1993 Pelotas (Brazil) Birth Cohort Study . J ATTEN DISORD 2022, 26 (3):331-339. 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Dalsgaard S, Mortensen PB, Frydenberg M, Maibing CM, Nordentoft M, Thomsen PH: Association between Attention-Deficit Hyperactivity Disorder in childhood and schizophrenia later in adulthood . EUR PSYCHIAT 2014, 29 (4):259-263. Weinstein AM: Computer and video game addiction-a comparison between game users and non-game users . AM J DRUG ALCOHOL AB 2010, 36 (5):268-276. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4266434\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":292340045,\"identity\":\"b888e7da-a51c-46d9-af76-f87ebbc1b4f3\",\"order_by\":0,\"name\":\"Qiong Fang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiong\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":292340047,\"identity\":\"16a82959-b9fd-472c-9f8f-017c18a5d6e4\",\"order_by\":1,\"name\":\"Yuehao Cai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuehao\",\"middleName\":\"\",\"lastName\":\"Cai\",\"suffix\":\"\"},{\"id\":292340048,\"identity\":\"a5371265-196b-432d-8637-1aa4e1201f98\",\"order_by\":2,\"name\":\"Jing Kang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jing\",\"middleName\":\"\",\"lastName\":\"Kang\",\"suffix\":\"\"},{\"id\":292340049,\"identity\":\"0ad25a8a-2177-47d3-b275-ebf20c20da47\",\"order_by\":3,\"name\":\"Yiyan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiyan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":292340050,\"identity\":\"e943937e-e9c1-40eb-b7c0-14099af39431\",\"order_by\":4,\"name\":\"Fubiao Ye\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACefb+h49/GPzjkWdvIFKLYc8ZZmOGigMyhj0HiLXmhg+bMMOZAzYMNxKI1ME4g/cYc2HbHR7GmY833mCosYkmqIVdui/t8cy2Zzzs0mnFFgzH0nIbCNoy54C5AW8bMw/j7BwzCcaGw4S1AL1gJgHSwnDzDNFacsykec4c5mG4wUOkFsOeY8mGMyrSeAx7gH5JIMYv8uzNBx98MLCxl2c/vPHGhxobIhyGBAwkEkhRDtFCqo5RMApGwSgYGQAAt9RBcC57IXsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Fujian Provincial Hospital, Provincial Clinical Medical College of Fujian Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Fubiao\",\"middleName\":\"\",\"lastName\":\"Ye\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-14 23:29:25\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4266434/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4266434/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":55317099,\"identity\":\"a3a8578a-a68c-4490-9581-6dcfe01be62d\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:50:39\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":95784,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy design and overview of Mendelian randomization (MR) study. GWAS: genome wide association study; ADHD: attention deficit hyperactivity disorder; SNP: single nucleotide polymorphism; MRC-IEU: Medical Research Council Integrative Epidemiology Unit; PGC: psychiatric genomics consortium; MAF: minor allele frequency; IV: instrumental variable; TSWT: time spent watching television; LMPU: length of mobile phone use;TSUC: time spent using computer; IVW: inverse variance weighted\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/c19802a6aa0a72ba9e3ec126.png\"},{\"id\":55318997,\"identity\":\"47c86ac4-7686-4872-99e3-6886961775fb\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:58:39\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87034,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe causal relationship between screen exposure time and ADHD. OR: odds ratio; CI: confidence interval; ADHD: attention deficit hyperactivity disorder; TSWT: time spent watching television; LMPU:length of mobile phone use; TSUC: time spent using computer.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/c9a930d57c81ed5ac8b3473a.png\"},{\"id\":55317097,\"identity\":\"53755db3-4d45-4695-b4dd-a514f11f98e0\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:50:39\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":390036,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe causal and sensitivity of analyses between TSWT and ADHD. (A) Scatter plot. The IVW method as a main effect implied that TSWT was at a relative risk of causing ADHD (IVW P\\u0026lt;0.00833). (B) Leave-one-out sensitivity analysis. After removing each SNP, all error bars were to the right of 0.00, indicating that the results were robust. (C) Forest plot of single-SNP MR. (D) Funnel plot. The distribution of causal effects presented in the funnel plot had essential symmetry. The nonsignificant bias was observed.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/8ea8cc8cce701109052eb90e.png\"},{\"id\":55317096,\"identity\":\"72a9d445-b9dc-446b-8eaf-213764fc0fc6\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:50:39\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":224156,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe causal and sensitivity of analyses between LMPU and ADHD. (A) Scatter plot. The IVW method showed that LMPU was at a relative risk of causing ADHD (IVW P\\u0026lt;0.00833). (B) Leave-one-out sensitivity analysis. After removing each SNP, all error bars were to the right of 0.00, implying that the results were robust. (C) Forest plot of single-SNP MR. (D) Funnel plot. The distribution of causal effects had essential symmetry. There was no significant bias.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/8681b61f15f0000be1b9d101.png\"},{\"id\":55317094,\"identity\":\"499c3018-7aae-4f92-b192-7a2cbc3ec0c7\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:50:39\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":341684,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe causal and sensitivity of analyses between TSUC and ADHD. (A) Scatter plot. The main effect showed no correlation between TSUC and ADHD (IVW P\\u0026gt;0.05). (B) Leave-one-out sensitivity analysis. After removing each SNP, all error bars were to the right of 0.00, indicating that the results were robust. (C) Forest plot of single-SNP MR. (D) Funnel plot. The distribution of causal effects presented in the funnel plot had essential symmetry, and no significant bias was observed.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/48ae80e048313460df96b494.png\"},{\"id\":56510558,\"identity\":\"5518dd01-7dc8-482f-a4e9-ed0cd9272776\",\"added_by\":\"auto\",\"created_at\":\"2024-05-15 06:29:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2375080,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4266434/v1/f8891f45-22f4-475b-bedb-709883fa95f6.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAttention 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%)[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The core symptoms consist of impairing inattention, hyperactivity, impulsivity, and inappropriate development [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. 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[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Screen exposure refers to the exposure and use of electronic products such as televisions, mobile phones and computers [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] 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 [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In addition, relevant studies have shown that premature exposure to electronic screen environments may increase the risk of developing ADHD [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Therefore, previous studies have found a time-dependent correlation between playing games on mobile phones and ADHD [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. 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.\\u003c/p\\u003e \\u003cp\\u003eMendelian 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 [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. In this study, the two-sample MR analysis was used to evaluate the causal relationship between screen exposure time and ADHD.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cp\\u003eThe 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.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Genome wide association study (GWAS) Summary Data for screen exposure time\\u003c/h2\\u003e \\u003cp\\u003eThree 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).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 GWAS Summary Data for ADHD\\u003c/h2\\u003e \\u003cp\\u003eThe 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).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Selection of IVs\\u003c/h2\\u003e \\u003cp\\u003eMR 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) (r\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), within a 1.0\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003ekb window, and meeting the whole genome significance level (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;5 \\u0026times; 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;8\\u003c/sup\\u003e) were selected. The strength of instrumental variables was evaluated through F-values (F\\u0026thinsp;=\\u0026thinsp;R\\u003csup\\u003e2\\u003c/sup\\u003e༏(1-R\\u003csup\\u003e2\\u003c/sup\\u003e) \\u0026times; [(N-K-1)/K]). Among them, R\\u003csup\\u003e2\\u003c/sup\\u003e represented the proportion of variation of IV explained by SNP, N represented the sample size, and K represented the number of SNPs. F\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 represented that the possibility of weak instrumental variable bias was relatively low [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 MR analysis\\u003c/h2\\u003e \\u003cp\\u003eThe 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 [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. 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\\u0026thinsp;=\\u0026thinsp;0.05/n\\u003csub\\u003eexpose\\u003c/sub\\u003e\\u0026middot;n\\u003csub\\u003eoutcome\\u003c/sub\\u003e). In addition, other methods, including MR-Egger, weighted median, simple mode and weighted mode, were also employed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Sensitivity analysis\\u003c/h2\\u003e \\u003cp\\u003eTo 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\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 indicated the presence of heterogeneity. A random effects model was used for MR analysis if heterogeneity existed [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. 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\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 indicates the existence of pleiotropy [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. 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.\\u003c/p\\u003e \\u003cp\\u003eThe above statistical analyses were implemented using the TwoSampleMR package and MR-PRESSO package in R (4.3.3) language software.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eIn 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.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 MR of TSWT on ADHD\\u003c/h2\\u003e \\u003cp\\u003eIVW and MR Egger tests were used to evaluate heterogeneity. The MR Egger test showed Q\\u0026thinsp;=\\u0026thinsp;166.4992, P\\u0026thinsp;=\\u0026thinsp;1.09E-05 and the IVW test showed Q\\u0026thinsp;=\\u0026thinsp;167.0688, P\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;3.454631, CI: 2.460256\\u0026ndash;4.850909, P\\u0026thinsp;=\\u0026thinsp;8.17611E-13\\u0026thinsp;\\u0026lt;\\u0026thinsp;0. 0.00833). TSWT may be a relative risk of causing ADHD.\\u003c/p\\u003e \\u003cp\\u003eThe 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\\u0026thinsp;=\\u0026thinsp;1.206160\\u0026ndash;1.299763, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;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.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 MR of LMPU on ADHD\\u003c/h2\\u003e \\u003cp\\u003eThe MR Egger test of LMPU on ADHD was Q\\u0026thinsp;=\\u0026thinsp;60.09084, P\\u0026thinsp;=\\u0026thinsp;0.000163046, and the IVW test was Q\\u0026thinsp;=\\u0026thinsp;62.51108, P\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;0.02196794, P\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;2.0063796 CI: 1.30737263-3.079121 P\\u0026thinsp;=\\u0026thinsp;0.001440136\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.00833). LMPU might be a risk factor for ADHD.\\u003c/p\\u003e \\u003cp\\u003eThe \\\"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\\u0026thinsp;=\\u0026thinsp;0.6275006\\u0026ndash;0.8143410, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). The results indicates that the MR results of LMPU on ADHD were reliable. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 MR of TSUC on ADHD\\u003c/h2\\u003e \\u003cp\\u003eThe MR Egger test was Q\\u0026thinsp;=\\u0026thinsp;101.0478, P\\u0026thinsp;=\\u0026thinsp;0.01106093, and the Inverse variance weighted test was Q\\u0026thinsp;=\\u0026thinsp;101.6827, P\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;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\\u0026thinsp;=\\u0026thinsp;0.741734, CI: 0.5354545\\u0026ndash;1.027481, P\\u0026thinsp;=\\u0026thinsp;0.07234459\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003eThe \\\"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\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). The result indicates that the MR results of LMPU on ADHD were reliable. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThe 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.\\u003c/p\\u003e \\u003cp\\u003ePrevious 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\\u0026thinsp;=\\u0026thinsp;596) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e] and a cross-sectional in young adults (n\\u0026thinsp;=\\u0026thinsp;408) also confirmed this conclusion [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e] 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 [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Through a retrospective questionnaire survey, it was found that prolonged screen exposure increases the risk of ADHD [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. 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.\\u003c/p\\u003e \\u003cp\\u003eAn 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 [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. 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.\\u003c/p\\u003e \\u003cp\\u003eAlthough 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 [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Attention deficit is related to the abnormal structure and function of the prefrontal cortex circuit in the brain [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. These theories were consistent with the results of this study, which suggested that early screen exposure might increase the risk of ADHD.\\u003c/p\\u003e \\u003cp\\u003eAt 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 [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. The FOXP2 gene discovered by GWAS was the only gene that overlaps with ADHD and telephone use [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. 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 [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. 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.\\u003c/p\\u003e \\u003cp\\u003eAlthough 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.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eIn 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.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eADHD: 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.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study did not require ethical approval and consent, as the research data were sourced from publicly available data compiled by GWAS.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data used in this study are publicly available. To assess the data, please contact the corresponding author.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declared no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe present study was sponsored by Natural Science\\u0026nbsp;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).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eQiong Fang participated in the study design,\\u0026nbsp;original-draft,\\u0026nbsp;methodology,\\u0026nbsp;data analysis and wrote the manuscript. Yuehao Cai, Jing Kang, and Yiyan Zhang participated in the\\u0026nbsp;investigation, methodology, and data analysis. Fubiao Ye contributed to design,\\u0026nbsp;supervision\\u0026nbsp;and revision. All authors have been involved in final\\u0026nbsp;version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe 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.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSalari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, Akbari H, Mohammadi M: \\u003cstrong\\u003eThe global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis\\u003c/strong\\u003e. \\u003cem\\u003eITAL J PEDIATR\\u003c/em\\u003e 2023, \\u003cstrong\\u003e49\\u003c/strong\\u003e(1):48.\\u003c/li\\u003e\\n\\u003cli\\u003eRajaprakash M, Leppert ML: \\u003cstrong\\u003eAttention-Deficit/Hyperactivity Disorder\\u003c/strong\\u003e. \\u003cem\\u003ePEDIATR REV\\u003c/em\\u003e 2022, \\u003cstrong\\u003e43\\u003c/strong\\u003e(3):135-147.\\u003c/li\\u003e\\n\\u003cli\\u003eSoares P, de Oliveira PD, Wehrmeister FC, Menezes A, Goncalves H: \\u003cstrong\\u003eIs Screen Time Throughout Adolescence Related to ADHD? 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Attention-Deficit Hyperactivity Disorder in childhood and schizophrenia later in adulthood\\u003c/strong\\u003e. \\u003cem\\u003eEUR PSYCHIAT\\u003c/em\\u003e 2014, \\u003cstrong\\u003e29\\u003c/strong\\u003e(4):259-263.\\u003c/li\\u003e\\n\\u003cli\\u003eWeinstein AM: \\u003cstrong\\u003eComputer and video game addiction-a comparison between game users and non-game users\\u003c/strong\\u003e. \\u003cem\\u003eAM J DRUG ALCOHOL AB\\u003c/em\\u003e 2010, \\u003cstrong\\u003e36\\u003c/strong\\u003e(5):268-276.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Attention deficit hyperactivity disorder, Screen media exposure, Mendelian randomization, Causal relationship\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4266434/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4266434/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e 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.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e 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. \\u003cbr\\u003e\\n \\u003cstrong\\u003eResults\\u003c/strong\\u003e 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.\\u003cbr\\u003e\\n \\u003cstrong\\u003eConclusion\\u003c/strong\\u003e 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.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The causal associations between screen exposure time and attention deficit hyperactivity disorder: a two-sample Mendelian randomization\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-25 15:50:34\",\"doi\":\"10.21203/rs.3.rs-4266434/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"4b4b7572-f9aa-42d3-ad97-d7b7d95bbe9d\",\"owner\":[],\"postedDate\":\"April 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-05-15T06:21:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-25 15:50:34\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4266434\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4266434\",\"identity\":\"rs-4266434\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}