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Screen time in the development of type 2 diabetes mellitus (T2DM) : a two-sample Mendelian randomization study

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Abstract

Objective:

This study aimed to investigate the potential causal relationship between screen time and the risk of developing type 2 diabetes mellitus (T2DM) using Mendelian randomization.

Methods:

Two-sample Mendelian randomization was conducted, utilizing genetic variants associated with different types of screen time as instrumental variables. Single nucleotide polymorphisms (SNPs) were used to assess the primary outcome, which was the risk of developing T2DM.

Results:

The analysis revealed a significant positive causal association between television viewing time and the risk of T2DM. Specifically, excessive television viewing time was found to increase the risk of developing T2DM (OR: 2.39, 95% CI: 1.90 to 3.00, P < 0.01). However, no significant causal relationship was observed between computer usage time and the risk of T2DM. Additionally, mobile phone use time showed a positive correlation with the risk of T2DM (OR: 1.31, 95% CI: 1.04 to 1.64, P = 0.02), albeit to a lesser extent than television viewing time.

Conclusion:

The findings of this study indicate a significant causal association between certain types of screen time, specifically television viewing and mobile phone use, and an increased risk of T2DM.

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Data availability

All the authors are very grateful for the data support provided by the IEU Open GWAS project.

References

  1. Y. Zheng, S.H. Ley, F.B. Hu, Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14(2), 88–98 (2018).

    Article  PubMed  Google Scholar 

  2. K. Ogurtsova et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res. Clin. Pract. 183, 109118 (2022).

    Article  PubMed  Google Scholar 

  3. A. Kumar, S.K. Bharti, A. Kumar, Type 2 diabetes mellitus: the concerned complications and target organs. Apollo Med. 11(3), 161–166 (2014).

    Article  Google Scholar 

  4. H. Hamasaki, Daily physical activity and type 2 diabetes: a review. World J. Diabet. 7(12), 243 (2016).

    Article  Google Scholar 

  5. Y. Shrivas et al. Increased paediatric screen time during pandemic: a cause of concern to child health. Indian J. Forensic Med. Toxicol. 14(4), 7056–7059 (2020).

    Google Scholar 

  6. M. Izabela, M. Agata, (Un) safe screen time? Critical theoretical-empirical analysis. Int. J. Pedag. Innov. N. Technol. 3(2), 47–52 (2016).

    Google Scholar 

  7. A.G. LeBlanc et al. The ubiquity of the screen: an overview of the risks and benefits of screen time in our modern world. Transl. J. Am. Coll. Sports Med. 2(17), 104–113 (2017).

    Google Scholar 

  8. A.E. Mark, I. Janssen, Relationship between screen time and metabolic syndrome in adolescents. J. Public Health 30(2), 153–160 (2008).

    Article  Google Scholar 

  9. J. Mcgavock, E. Sellers, H. Dean, Physical activity for the prevention and management of youth-onset type 2 diabetes mellitus: focus on cardiovascular complications. Diabetes Vasc. Dis. Res. 4(4), 305–310 (2007).

    Article  Google Scholar 

  10. S.J. Biddle et al. A randomised controlled trial to reduce sedentary time in young adults at risk of type 2 diabetes mellitus: project STAND (Sedentary Time ANd Diabetes). PLoS One 10(12), e0143398 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. H.-T. Kang et al. Association between screen time and metabolic syndrome in children and adolescents in Korea: the 2005 Korean National Health and Nutrition Examination Survey. Diabetes Res. Clin. Pract. 89(1), 72–78 (2010).

    Article  PubMed  Google Scholar 

  12. R.A. DeFronzo et al. Type 2 diabetes mellitus. Nat. Rev. Dis. Prim. 1(1), 1–22 (2015).

    Google Scholar 

  13. J.E. Gale et al. Disruption of circadian rhythms accelerates development of diabetes through pancreatic beta-cell loss and dysfunction. J. Biol. Rhythms 26(5), 423–433 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  14. L.K. Fonken, R.J. Nelson, The effects of light at night on circadian clocks and metabolism. Endocr. Rev. 35(4), 648–670 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. I. Kyrou et al. Sociodemographic and lifestyle-related risk factors for identifying vulnerable groups for type 2 diabetes: a narrative review with emphasis on data from Europe. BMC Endocr. Disord. 20, 1–13 (2020).

    Article  Google Scholar 

  16. R. Boulos et al. ObesiTV: how television is influencing the obesity epidemic. Physiol. Behav. 107(1), 146–153 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. A. Astrup, Healthy lifestyles in Europe: prevention of obesity and type II diabetes by diet and physical activity. Public Health Nutr. 4(2b), 499–515 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. V. Gupta, G. Walia, M. Sachdeva, Mendelian randomization’: an approach for exploring causal relations in epidemiology. Public Health 145, 113–119 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. P. Sekula et al. Mendelian randomization as an approach to assess causality using observational data. J. Am. Soc. Nephrol. 27(11), 3253–3265 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. S.N. Islam et al. Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review. BMC Med. Res. Methodol. 22(1), 1–8 (2022).

    Article  MathSciNet  Google Scholar 

  21. R.C. Richmond, G.D. Smith, Mendelian randomization: concepts and scope. Cold Spring Harb. Perspect. Med. 12(1), a040501 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. L.J. Howe et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. 54(5), 581–592 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. J. Bowden et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int. J. Epidemiol. 45(6), 1961–1974 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. W. Spiller, N.M. Davies, T.M. Palmer, Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses. Int. J. Epidemiol. 48(3), 684 (2019).

    Article  Google Scholar 

  25. C.J. Patel, J. Bhattacharya, A.J. Butte, An environment-wide association study (EWAS) on type 2 diabetes mellitus. PloS one 5(5), e10746 (2010).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  26. L. Skøt, J.B. Nielsen, A. Leppin, Who perceives a higher personal risk of developing type 2 diabetes? A cross-sectional study on associations between personality traits, health-related behaviours and perceptions of susceptibility among university students in Denmark. BMC Public Health 18(1), 1–10 (2018).

    Article  Google Scholar 

  27. S. Jain et al. Prevalence and determinants of excessive screen viewing time in children aged 3–15 Years and Its effects on physical activity, sleep, eye symptoms and headache. Int. J. Environ. Res. Public Health 20(4), 3449 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  28. S. Cassidy et al. Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233,110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes. BMJ Open 6(3), e010038 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  29. A. Grøntved, F.B. Hu, Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta-analysis. Jama 305(23), 2448–2455 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  30. D.S. Bickham et al. Characteristics of screen media use associated with higher BMI in young adolescents. Pediatrics 131(5), 935–941 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. L.A. Manwell et al. Digital dementia in the internet generation: excessive screen time during brain development will increase the risk of Alzheimer’s disease and related dementias in adulthood. J. Integr. Neurosci. 21(1), 28 (2022).

    Article  PubMed  Google Scholar 

  32. K. Fang et al. Screen time and childhood overweight/obesity: a systematic review and meta‐analysis. Child.: Care, Health Dev. 45(5), 744–753 (2019).

    Article  PubMed  Google Scholar 

  33. G. Qi, N. Chatterjee, Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat. Commun. 10(1), 1941 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  34. G.D. Smith, Mendelian randomization for strengthening causal inference in observational studies: application to gene× environment interactions. Perspect. Psychol. Sci. 5(5), 527–545 (2010).

    Article  PubMed  Google Scholar 

  35. N. Allen et al. UK biobank: current status and what it means for epidemiology. Health Policy Technol. 1(3), 123–126 (2012).

    Article  Google Scholar 

  36. A. Xue et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9(1), 2941 (2018).

    Article  MathSciNet  PubMed  PubMed Central  ADS  Google Scholar 

  37. X. Li et al. Replacement of sedentary behavior by various daily-life physical activities and structured exercises: genetic risk and incident type 2 diabetes. Diab. Care 44(10), 2403–2410 (2021).

    Article  Google Scholar 

  38. S. Ikehara et al. Television viewing time, walking time, and risk of type 2 diabetes in Japanese men and women: The Japan Collaborative Cohort Study. Prevent. Med. 118, 220–225 (2019).

    Article  Google Scholar 

  39. F.B. Hu et al. Physical activity and television watching in relation to risk for type 2 diabetes mellitus in men. Arch. Intern. Med. 161(12), 1542–1548 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. A.A. Thorp et al. Independent and joint associations of TV viewing time and snack food consumption with the metabolic syndrome and its components; a cross-sectional study in Australian adults. Int. J. Behav. Nutr. Phys. Act. 10(1), 1–11 (2013).

    Article  Google Scholar 

  41. E. Fletcher et al. Is the relationship between sedentary behaviour and cardiometabolic health in adolescents independent of dietary intake? A systematic review. Obes. Rev. 16(9), 795–805 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. B. Jing et al. Associations of sedentary time and physical activity with metabolic syndrome among chinese adults: Results from the china health and nutrition survey. Biomed. Environ. Sci. 34(12), 963–975 (2021).

    Google Scholar 

  43. F. Imamura et al. Risk factors for type 2 diabetes mellitus preceded by β-cell dysfunction, insulin resistance, or both in older adults: the Cardiovascular Health Study. Am. J. Epidemiol. 177(12), 1418–1429 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. J.-P. Chaput et al. Sleep duration as a risk factor for the development of type 2 diabetes or impaired glucose tolerance: analyses of the Quebec Family Study. Sleep. Med. 10(8), 919–924 (2009).

    Article  PubMed  Google Scholar 

  45. F.B. Hu et al. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. Jama 289(14), 1785–1791 (2003).

    Article  PubMed  Google Scholar 

  46. C.D. Studebaker, B.P. Murphy, Prolonged sitting: current concepts on the physiological effects of seated postures at work. Professional Saf. 59(09), 42–48 (2014).

    Google Scholar 

  47. D.W. Dunstan et al. Television viewing time and mortality: the Australian diabetes, obesity and lifestyle study (AusDiab). Circulation 121(3), 384–391 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. A. Molla, P.S. Licker, eCommerce adoption in developing countries: a model and instrument. Inf. Manag. 42(6), 877–899 (2005).

    Article  Google Scholar 

  49. K. Kim et al. The Relation Between eHealth Literacy and Health-Related Behaviors: Systematic Review and Meta-analysis. J. Med. Internet Res. 25, e40778 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  50. S. Thomée, A. Härenstam, M. Hagberg, Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC public health 11(1), 1–11 (2011).

    Article  Google Scholar 

  51. Y. Kang et al. Testing the bidirectional associations of mobile phone addiction behaviors with mental distress, sleep disturbances, and sleep patterns: a one-year prospective study among Chinese college students. Front. psychiatry 11, 634 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. M. Briguglio et al. Healthy Eating, Physical Activity, and Sleep Hygiene (HEPAS) as the Winning Triad for Sustaining Physical and Mental Health in Patients at Risk for or with Neuropsychiatric Disorders: Considerations for Clinical Practice. Neuropsychiatr. Dis. Treat. 16, 55–70 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

Authors

Contributions

Concept and design: Z.Q. and X.J. Acquisition, analysis, and interpretation of data: Z.Q, Y.F., Y.L. Drafting of the manuscript: Y.X. and X.J. Critical revision of the manuscript for important intellectual content: Z.Q., X.J. and Y.F. Statistical analysis: Z.Q. and X.J. Visualization, Z.Q., X.J. and Y.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ying Xiao.

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Qiu, Z., Jia, X., Li, Y. et al. Screen time in the development of type 2 diabetes mellitus (T2DM) : a two-sample Mendelian randomization study. Endocrine (2024). https://doi.org/10.1007/s12020-024-03723-5

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