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Causal effects of diabetic retinopathy on depression, anxiety and bipolar disorder in the European population: a Mendelian randomization study

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Abstract

Purpose

To verify the causal effects of diabetic retinopathy (DR) on depression, anxiety and bipolar disorder (BD).

Methods

Mendelian randomization (MR) analysis was performed to identify the causal relationships between DR and depression or anxiety or BD via using DR-related GWAS data (14,584 cases and 176,010 controls), depression-related GWAS data (59,851 cases and 113,154 controls), anxiety-related GWAS data (7016 cases and 14,745 controls) and BD-related GWAS data (41,917 cases and 371,549 controls). The inverse-variance weighted (IVW) model was adopted to estimate the causal relationship. The outcome was expressed as odds ratio (OR) with 95% confidence intervals (CI).

Results

The MR analysis results presented that DR was causally associated with a significantly increased risk of BD in the European population (IVW, OR = 1.06, 95%CI [1.03, 1.08], P = 2.44 × 10−6), while DR was unable to causally influence the risk of depression (IVW, OR = 1.01, 95%CI [0.99, 1.04], P = 0.32) and anxiety (IVW, OR = 0.97, 95%CI [0.89, 1.06], P = 0.48) in the European population. Subgroup analysis based on BD identified DR causally increased the risk of bipolar I disorder (BD I) but not bipolar II disorder (BD II). Sensitivity analysis results did not show any pleiotropy and heterogeneity in both groups of analyses, indicating that the results were stable and reliable.

Conclusions

The results of the current MR analysis indicated a causal relationship between DR and BD in the European population, while there was no causal connection between DR and depression or anxiety. However, further research is needed to confirm these conclusions.

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Data availability

The data presented in this study are available in article and supplementary material.

References

  1. Cheung N, Mitchell P, Wong TY (2010) Diabetic retinopathy. Lancet 376(9735):124–136

    Article  PubMed  Google Scholar 

  2. Lee R, Wong TY, Sabanayagam C (2015) Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond) 2:17

    Article  PubMed  Google Scholar 

  3. Teo ZL et al (2021) Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: systematic review and meta-analysis. Ophthalmology 128(11):1580–1591

    Article  PubMed  Google Scholar 

  4. Milluzzo A et al (2021) Epigenetic mechanisms in type 2 diabetes retinopathy: a systematic review. Int J Mol Sci 22(19):10502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Milluzzo A et al (2022) Do nutrients and nutraceuticals play a role in diabetic retinopathy? A systematic review. Nutrients 14(20):4430

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sun XJ et al (2022) Associations between psycho-behavioral risk factors and diabetic retinopathy: NHANES (2005–2018). Front Public Health 10:966714

    Article  PubMed  PubMed Central  Google Scholar 

  7. Deswal J et al (2020) To study the impact of diabetic retinopathy on quality of life in Indian diabetic patients. Indian J Ophthalmol 68(5):848–853

    Article  PubMed  PubMed Central  Google Scholar 

  8. McCarron RM et al (2021) Depression. Ann Intern Med 174(5):itc65–itc80

    Article  PubMed  Google Scholar 

  9. Smith K (2014) Mental health: a world of depression. Nature 515(7526):181

    Article  PubMed  ADS  Google Scholar 

  10. Szuhany KL, Simon NM (2022) Anxiety disorders: a review. JAMA 328(24):2431–2445

    Article  CAS  PubMed  Google Scholar 

  11. Matcham F et al (2014) Self-help interventions for symptoms of depression, anxiety and psychological distress in patients with physical illnesses: a systematic review and meta-analysis. Clin Psychol Rev 34(2):141–157

    Article  CAS  PubMed  Google Scholar 

  12. Lyu Z et al (2023) Associations of concomitant retinopathy and depression with mortality in a nationally representative population. J Affect Disord 336:15–24

    Article  PubMed  Google Scholar 

  13. Xu L et al (2022) Prevalence and associated factors of depression and anxiety among Chinese diabetic retinopathy patients: a cross-sectional study. PLoS ONE 17(4):e0267848

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rees G et al (2016) Association between diabetes-related eye complications and symptoms of anxiety and depression. JAMA Ophthalmol 134(9):1007–1014

    Article  PubMed  Google Scholar 

  15. Trento M et al (2014) Depression, anxiety, cognitive impairment and their association with clinical and demographic variables in people with type 2 diabetes: a 4-year prospective study. J Endocrinol Invest 37(1):79–85

    Article  CAS  PubMed  Google Scholar 

  16. Morjaria R et al (2019) Impact of diabetic retinopathy on sleep, mood, and quality of life. Invest Ophthalmol Vis Sci 60(6):2304–2310

    Article  PubMed  PubMed Central  Google Scholar 

  17. Anderson IM, Haddad PM, Scott J (2012) Bipolar disorder. BMJ 345:e8508

    Article  PubMed  Google Scholar 

  18. Diler RS et al (2013) Neural activity to intense positive versus negative stimuli can help differentiate bipolar disorder from unipolar major depressive disorder in depressed adolescents: a pilot fMRI study. Psychiatry Res 214(3):277–284

    Article  PubMed  Google Scholar 

  19. Chen J et al (2021) Neurometabolic alterations in bipolar disorder with anxiety symptoms: a proton magnetic resonance spectroscopy study of the prefrontal whiter matter. Psychiatry Res 299:113859

    Article  CAS  PubMed  Google Scholar 

  20. Lawlor DA et al (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27(8):1133–1163

    Article  MathSciNet  PubMed  Google Scholar 

  21. Burgess S, Small DS, Thompson SG (2017) A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res 26(5):2333–2355

    Article  MathSciNet  PubMed  Google Scholar 

  22. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658–665

    Article  PubMed  PubMed Central  Google Scholar 

  23. Smith GD, Ebrahim S (2003) “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32(1):1–22

    Article  PubMed  Google Scholar 

  24. Hemani G et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7:e34408

    Article  PubMed  PubMed Central  Google Scholar 

  25. Swerdlow DI et al (2016) Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int J Epidemiol 45(5):1600–1616

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wray NR et al (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50(5):668–681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Otowa T et al (2016) Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry 21(10):1391–1399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mullins N et al (2021) Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet 53(6):817–829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Skrivankova VW et al (2021) Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA 326(16):1614–1621

    Article  PubMed  Google Scholar 

  30. Boef AG, Dekkers OM, le Cessie S (2015) Mendelian randomization studies: a review of the approaches used and the quality of reporting. Int J Epidemiol 44(2):496–511

    Article  PubMed  Google Scholar 

  31. Palmer TM et al (2012) Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 21(3):223–242

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  32. Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755–764

    Article  PubMed  Google Scholar 

  33. Frasure-Smith N, Lespérance F, Talajic M (1993) Depression following myocardial infarction. Impact on 6-month survival. JAMA 270(15):1819–1825

    Article  CAS  PubMed  Google Scholar 

  34. Liblik K et al (2022) Depression and anxiety following acute myocardial infarction in women. Trends Cardiovasc Med 32(6):341–347

    Article  PubMed  Google Scholar 

  35. Klein R et al (2002) The association of atherosclerosis, vascular risk factors, and retinopathy in adults with diabetes: the atherosclerosis risk in communities study. Ophthalmology 109(7):1225–1234

    Article  PubMed  Google Scholar 

  36. Verbanck M et al (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hartwig FP, Davey Smith G, Bowden J (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46(6):1985–1998

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hemani G, Bowden J, Davey Smith G (2018) Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet 27(R2):R195–R208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bowden J, Holmes MV (2019) Meta-analysis and Mendelian randomization: a review. Res Synth Methods 10(4):486–496

    Article  PubMed  PubMed Central  Google Scholar 

  40. Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32(5):377–389

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bowden J et al (2019) Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol 48(3):728–742

    Article  PubMed  Google Scholar 

  42. Emdin CA, Khera AV, Kathiresan S (2017) Mendelian randomization. JAMA 318(19):1925–1926

    Article  PubMed  Google Scholar 

  43. Chen C et al (2023) Pharmacological roles of lncRNAs in diabetic retinopathy with a focus on oxidative stress and inflammation. Biochem Pharmacol 214:115643

    Article  CAS  PubMed  Google Scholar 

  44. Nguyen TT et al (2008) Is depression associated with microvascular disease in patients with type 2 diabetes? Depress Anxiety 25(11):E158–E162

    Article  PubMed  Google Scholar 

  45. Dirmaier J et al (2010) Diabetes in primary care: prospective associations between depression, nonadherence and glycemic control. Psychother Psychosom 79(3):172–178

    Article  PubMed  Google Scholar 

  46. Carvalho AF, Firth J, Vieta E (2020) Bipolar disorder. N Engl J Med 383(1):58–66

    Article  CAS  PubMed  Google Scholar 

  47. Lamoureux EL, Hassell JB, Keeffe JE (2004) The determinants of participation in activities of daily living in people with impaired vision. Am J Ophthalmol 137(2):265–270

    Article  PubMed  Google Scholar 

  48. Khoo K et al (2019) The relationship between diabetic retinopathy and psychosocial functioning: a systematic review. Qual Life Res 28(8):2017–2039

    Article  PubMed  Google Scholar 

  49. Mansur RB et al (2016) Impaired glucose metabolism moderates the course of illness in bipolar disorder. J Affect Disord 195:57–62

    Article  CAS  PubMed  Google Scholar 

  50. Watson S et al (2004) Hypothalamic-pituitary-adrenal axis function in patients with bipolar disorder. Br J Psychiatry 184:496–502

    Article  PubMed  Google Scholar 

  51. Hajek T, McIntyre R, Alda M (2016) Bipolar disorders, type 2 diabetes mellitus, and the brain. Curr Opin Psychiatry 29(1):1–6

    Article  PubMed  Google Scholar 

  52. Hernández-Moreno L et al (2021) Is perceived social support more important than visual acuity for clinical depression and anxiety in patients with age-related macular degeneration and diabetic retinopathy? Clin Rehabil 35(9):1341–1347

    Article  PubMed  PubMed Central  Google Scholar 

  53. Salk RH, Hyde JS, Abramson LY (2017) Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull 143(8):783–822

    Article  PubMed  PubMed Central  Google Scholar 

  54. Rhebergen D et al (2017) Admixture analysis of age of onset in generalized anxiety disorder. J Anxiety Disord 50:47–51

    Article  PubMed  Google Scholar 

  55. Almeida OP, Fenner S (2002) Bipolar disorder: similarities and differences between patients with illness onset before and after 65 years of age. Int Psychogeriatr 14(3):311–322

    Article  PubMed  Google Scholar 

  56. Leibenluft E (1996) Women with bipolar illness: clinical and research issues. Am J Psychiatry 153(2):163–173

    Article  CAS  PubMed  Google Scholar 

  57. Ducat L et al (2015) A review of the mental health issues of diabetes conference. Diabetes Care 38(2):333–338

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We sincerely thank FinnGen consortium, Ieu Open Gwas Project and Psychiatric Genomics Consortium for publicly providing all the data for this MR analysis.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, Tangdu Hospital, The Air Force Military Medical University, 569 Xinsi Rd, Xi’an, 710038, China

    C. Chen & J. Han

  2. Department of Ophthalmology, The 900th Hospital of Joint Logistic Support Force, PLA (Clinical Medical College of Fujian Medical University, Dongfang Hospital Affiliated to Xiamen University), Fuzhou, 350025, China

    C. Chen & W. Yan

  3. College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China

    Y. Lan

  4. Department of Thoracic Surgery, Tangdu Hospital, The Air Force Military Medical University, Xi’an, 710038, China

    Z. Wang & X. Yan

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  1. C. Chen
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Contributions

Original draft writing, CC; Data analysis, CC and YL; Idea: JH and XY; Scheme design: CC and ZW; Graphical design, CC, WY, ZW. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to X. Yan or J. Han.

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Conflict of interest

Chengming Chen, Yanyan Lan, Zhaoyang Wang, Xiaolong Yan, and Jing Han declare no interest conflicts among them.

Ethics approval

Since the data adopted in this MR analysis were all publicly available data from the Finngen database, Ieu Open Gwas Project and Psychiatric Genomics Consortium, all data-related studies were approved by their respective ethical review committees and received written informed consent from patients. Therefore, this study does not need additional ethics approval.

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Chen, C., Lan, Y., Wang, Z. et al. Causal effects of diabetic retinopathy on depression, anxiety and bipolar disorder in the European population: a Mendelian randomization study. J Endocrinol Invest 47, 585–592 (2024). https://doi.org/10.1007/s40618-023-02176-3

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  • DOI: https://doi.org/10.1007/s40618-023-02176-3

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