Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Association between major depressive disorder and multiple disease outcomes: a phenome-wide Mendelian randomisation study in the UK Biobank


Depression affects all aspects of an individual’s life but evidence relating to the causal effects on health is limited. We used information from 337,536 UK Biobank participants and performed hypothesis-free phenome-wide association analyses between major depressive disorder (MDD) genetic risk score (GRS) and 925 disease outcomes. GRS–disease outcome associations passing the multiple-testing corrected significance threshold (P < 1.9 × 10−3) were followed by Mendelian randomisation (MR) analyses to test for causality. MDD GRS was associated with 22 distinct diseases in the phenome-wide discovery stage, with the strongest signal observed for MDD diagnosis and related co-morbidities including anxiety and sleep disorders. In inverse-variance weighted MR analyses, MDD was associated with several inflammatory and haemorrhagic gastrointestinal diseases, including oesophagitis (OR 1.32, 95% CI 1.18–1.48), non-infectious gastroenteritis (OR 1.25, 95% CI 1.06–1.48), gastrointestinal haemorrhage (OR 1.26, 95% CI 1.11–1.43) and intestinal E.coli infections (OR 3.24, 95% CI 1.74–6.02). Signals were also observed for symptoms/disorders of the urinary system (OR 1.36, 95% CI 1.19–1.56), asthma (OR 1.23, 95% CI 1.06–1.44), and painful respiration (OR 1.28, 95% CI 1.14–1.44). MDD was associated with disorders of lipid metabolism (OR 1.22, 95% CI 1.12–1.34) and ischaemic heart disease (OR 1.30, 95% CI 1.15–1.47). Sensitivity analyses excluding pleiotropic variants provided consistent associations. Our study indicates a causal link between MDD and a broad range of diseases, suggesting a notable burden of co-morbidity. Early detection and management of MDD is important, and treatment strategies should be selected to also minimise the risk of related co-morbidities.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2


  1. 1.

    Depression and other common mental disorders: Global health estimates. 2017 Accessed 2017.

  2. 2.

    Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76:155–62.

    Article  Google Scholar 

  3. 3.

    Gaebel W, Kowitz S, Fritze J, Zielasek J. Use of health care services by people with mental illness: secondary data from three statutory health insurers and the German Statutory Pension Insurance Scheme. Dtsch Arztebl Int. 2013;110:799–808.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Emmanuel J, Simmonds S, Tyrer P. Systematic review of the outcome of anxiety and depressive disorders. Br J Psychiatry Suppl. 1998;34:35–41.

    Article  Google Scholar 

  5. 5.

    Dumbreck S, Flynn A, Nairn M, Wilson M, Treweek S, Mercer SW, et al. Drug-disease and drug-drug interactions: systematic examination of recommendations in 12 UK national clinical guidelines. Br Med J. 2015;350:h949.

    Article  Google Scholar 

  6. 6.

    Russo F. Public health policy, evidence, and causation: lessons from the studies on obesity. Med Health Care Philos. 2012;15:141–51.

    Article  Google Scholar 

  7. 7.

    Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22.

    Article  Google Scholar 

  8. 8.

    Smith DG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98.

    Article  Google Scholar 

  9. 9.

    Denny JC, Bastarache L, Roden DM. Phenome-wide association studies as a tool to advance precision medicine. Annu Rev Genom Hum Genet. 2016;17:353–73.

    CAS  Article  Google Scholar 

  10. 10.

    Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    CAS  Article  Google Scholar 

  11. 11.

    Millard AC, Davies NM, Timpson NJ, Tilling K, Flach PA, Smith GD. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep. 2015;5:16645.

    CAS  Article  Google Scholar 

  12. 12.

    Hewitt J, Walters M, Padmanabhan S, Dawson J. Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease. BMJ Open. 2016;6:e009161.

    CAS  Article  Google Scholar 

  13. 13.

    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.

    CAS  Article  Google Scholar 

  14. 14.

    Wei WQ, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ, Gamazon ER, et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS ONE. 2017;12:e0175508.

    Article  Google Scholar 

  15. 15.

    Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31:1102–10.

    CAS  Article  Google Scholar 

  16. 16.

    Verma A, Bradford Y, Dudek S, Lucas AM, Verma SS, Pendergrass SA, et al. A simulation study investigating power estimates in phenome-wide association studies. BMC Bioinf. 2018;19:120.

    Article  Google Scholar 

  17. 17.

    Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48:1031–6.

    CAS  Article  Google Scholar 

  18. 18.

    Carroll RJ, Bastarache L, Denny JC. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics. 2014;30:2375–6.

    CAS  Article  Google Scholar 

  19. 19.

    Yoav Benjamini a, Hochberg Y. Controlling the false discovery rate: a practictical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.

    Google Scholar 

  20. 20.

    Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  Google Scholar 

  21. 21.

    Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    CAS  Article  Google Scholar 

  22. 22.

    Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry. 2016;21:1391–9.

    CAS  Article  Google Scholar 

  23. 23.

    Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122.

    CAS  Article  Google Scholar 

  24. 24.

    Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–30.

    CAS  Article  Google Scholar 

  25. 25.

    Martin-Merino E, Ruigomez A, Garcia Rodriguez LA, Wallander MA, Johansson S. Depression and treatment with antidepressants are associated with the development of gastro-oesophageal reflux disease. Aliment Pharmacol Ther. 2010;31:1132–40.

    CAS  PubMed  Google Scholar 

  26. 26.

    Jiang HY, Chen HZ, Hu XJ, Yu ZH, Yang W, Deng M, et al. Use of selective serotonin reuptake inhibitors and risk of upper gastrointestinal bleeding: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2015;13:42–50.e43.

    CAS  Article  Google Scholar 

  27. 27.

    Carvalho AF, Sharma MS, Brunoni AR, Vieta E, Fava GA. The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: a critical review of the literature. Psychother Psychosom. 2016;85:270–88.

    Article  Google Scholar 

  28. 28.

    Kohler S, Cierpinsky K, Kronenberg G, Adli M. The serotonergic system in the neurobiology of depression: Relevance for novel antidepressants. J Psychopharmacol. 2016;30:13–22.

    Article  Google Scholar 

  29. 29.

    Santarsieri D, Schwartz TL. Antidepressant efficacy and side-effect burden: a quick guide for clinicians. Drugs Context. 2015;4:212290.

    Article  Google Scholar 

  30. 30.

    Slekiene J, Mosler HJ. Does depression moderate handwashing in children? BMC Public Health. 2017;18:82.

    Article  Google Scholar 

  31. 31.

    Gao YH, Zhao HS, Zhang FR, Gao Y, Shen P, Chen RC, et al. The relationship between depression and asthma: a meta-analysis of prospective studies. PLoS ONE. 2015;10:e0132424.

    Article  Google Scholar 

  32. 32.

    Rugulies R. Depression as a predictor for coronary heart disease. a review and meta-analysis. Am J Prev Med. 2002;23:51–61.

    Article  Google Scholar 

  33. 33.

    Wulsin LR, Singal BM. Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychosom Med. 2003;65:201–10.

    Article  Google Scholar 

  34. 34.

    Nicholson A, Kuper H, Hemingway H. Depression as an aetiologic and prognostic factor in coronary heart disease: a meta-analysis of 6362 events among 146 538 participants in 54 observational studies. Eur Heart J. 2006;27:2763–74.

    Article  Google Scholar 

  35. 35.

    Van der Kooy K, van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A. Depression and the risk for cardiovascular diseases: systematic review and meta analysis. Int J Geriatr Psychiatry. 2007;22:613–26.

    Article  Google Scholar 

  36. 36.

    Gan Y, Gong Y, Tong X, Sun H, Cong Y, Dong X, et al. Depression and the risk of coronary heart disease: a meta-analysis of prospective cohort studies. BMC Psychiatry. 2014;14:371.

    Article  Google Scholar 

  37. 37.

    Ference BA, Yoo W, Alesh I, Mahajan N, Mirowska KK, Mewada A, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. J Am Coll Cardiol. 2012;60:2631–9.

    CAS  Article  Google Scholar 

  38. 38.

    MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45:D896–D901.

    CAS  Article  Google Scholar 

  39. 39.

    Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2012;186:1026–34.

    Article  Google Scholar 

  40. 40.

    Hughes RA, Davies NM, Smith GD, Tilling K. Selection bias when estimting average treatment effects using one-sample instrumental variable analysis. Epidemiology. 2019; 30:350–57.

Download references


The authors are very grateful to the UK Biobank and UK Biobank Participants. We conducted this research using the UK Biobank Resource under application number 10171. The authors are also thankful to the Australian Research Training Program Scholarship for AM’s studentship fund/support.

Author information




EH and AM conceptualised the study. AM and AZ undertook data management and analyses. AM and EH drafted the paper. All authors interpreted the results, critically revised the paper for intellectual content and approved the final paper.

Corresponding author

Correspondence to Elina Hyppönen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mulugeta, A., Zhou, A., King, C. et al. Association between major depressive disorder and multiple disease outcomes: a phenome-wide Mendelian randomisation study in the UK Biobank. Mol Psychiatry 25, 1469–1476 (2020).

Download citation

Further reading


Quick links