Genetically determined serum urate levels and cardiovascular and other diseases in UK Biobank cohort: A phenome-wide mendelian randomization study

English version

Autoři: Xue Li ^aff001; Xiangrui Meng ^aff001; Yazhou He ^aff001; Athina Spiliopoulou ^aff002; Maria Timofeeva ^aff003; Wei-Qi Wei ^aff004; Aliya Gifford ^aff004; Tian Yang ^aff001; Tim Varley ^aff005; Ioanna Tzoulaki ^aff006; Peter Joshi ^aff001; Joshua C. Denny ^aff004; Paul Mckeigue ^aff002; Harry Campbell ^aff001; Evropi Theodoratou ^aff001
Působiště autorů: Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom ^aff001; Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom ^aff002; Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom ^aff003; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America ^aff004; Public Health and Intelligence, NHS National Services Scotland, Edinburgh, United Kingdom ^aff005; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom ^aff006; Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece ^aff007; Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom ^aff008
Vyšlo v časopise: Genetically determined serum urate levels and cardiovascular and other diseases in UK Biobank cohort: A phenome-wide mendelian randomization study. PLoS Med 16(10): e32767. doi:10.1371/journal.pmed.1002937
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1002937

Souhrn

Background

The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance.

Methods and findings

A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40–69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10⁻⁴). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization.

Conclusion

In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

Klíčová slova:

Genetic loci – Genome-wide association studies – Coronary heart disease – Hypertension – Cardiovascular diseases – Genetics of disease – Consortia – Gout

Zdroje

1. Li X, Meng X, Timofeeva M, Tzoulaki I, Tsilidis KK, Ioannidis JP, et al. Serum uric acid levels and multiple health outcomes: umbrella review of evidence from observational studies, randomised controlled trials, and Mendelian randomisation studies. BMJ. 2017;357:j2376. doi: 10.1136/bmj.j2376 28592419

2. Feig DI, Kang DH, Johnson RJ. Uric Acid and Cardiovascular Risk. N Engl J Med. 2008;359(17):1811–21. doi: 10.1056/NEJMra0800885 18946066

3. Borghi C, Rosei EA, Bardin T, Dawson J, Dominiczak A, Kielstein JT, et al. Serum uric acid and the risk of cardiovascular and renal disease. J Hypertens. 2015;33(9):1729–41;. doi: 10.1097/HJH.0000000000000701 26136207

4. Mazzali M, Kanbay M, Segal MS, Shafiu M, Jalal D, Feig DI, et al. Uric acid and hypertension: cause or effect? Curr Rheumatol Rep. 2010;12(2):108–17. doi: 10.1007/s11926-010-0094-1 20425019

5. Emmerson BT, Nagel SL, Duffy DL, Martin NG. Genetic control of the renal clearance of urate: a study of twins. Ann Rheum Dis. 1992;51(3):375–7. doi: 10.1136/ard.51.3.375 1575585

6. Wilk JB, Djousse L, Borecki I, Atwood LD, Hunt SC, Rich SS, et al. Segregation analysis of serum uric acid in the NHLBI Family Heart Study. Hum Genet. 2000;106(3):355–9. doi: 10.1007/s004390051050 10798367

7. Kolz M, Johnson T, Sanna S, Teumer A, Vitart V, Perola M, et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet. 2009;5(6):e1000504. doi: 10.1371/journal.pgen.1000504 19503597

8. Kottgen A, Albrecht E, Teumer A, Vitart V, Krumsiek J, Hundertmark C, et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat Genet. 2013;45(2):145–54. doi: 10.1038/ng.2500 23263486

9. Dehghan A, Kottgen A, Yang Q, Hwang SJ, Kao WL, Rivadeneira F, et al. Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study. Lancet. 2008;372(9654):1953–61. doi: 10.1016/S0140-6736(08)61343-4 18834626

10. Vitart V, Rudan I, Hayward C, Gray NK, Floyd J, Palmer CN, et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout. Nat Genet. 2008;40(4):437–42. doi: 10.1038/ng.106 18327257

11. White J, Sofat R, Hemani G, Shah T, Engmann J, Dale C, et al. Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis. Lancet Diabetes Endocrinol. 2016;4(4):327–36. doi: 10.1016/S2213-8587(15)00386-1 26781229

12. Palmer TM, Nordestgaard BG, Benn M, Tybjaerg-Hansen A, Davey Smith G, Lawlor DA, et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts. BMJ. 2013;347:f4262. doi: 10.1136/bmj.f4262 23869090

13. Kleber ME, Delgado G, Grammer TB, Silbernagel G, Huang J, Kramer BK, et al. Uric Acid and Cardiovascular Events: A Mendelian Randomization Study. J Am Soc Nephrol. 2015;26(11):2831–8. doi: 10.1681/ASN.2014070660 25788527

14. Jordan DM, Choi HK, Verbanck M, Topless R, Won HH, Nadkarni G, et al. No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study. PLoS Med. 2019; 16(1):e1002725. doi: 10.1371/journal.pmed.1002725 30645594

15. Li X, Meng X, Spiliopoulou A, Timofeeva M, Wei WQ, Gifford A, et al. MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank. Ann Rheum Dis. 2018; 77(7): 1039–47. doi: 10.1136/annrheumdis-2017-212534 29437585

16. Cortes A, Dendrou CA, Motyer A, Jostins L, Vukcevic D, Dilthey A, et al. Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank. Nat Genet. 2017;49(9):1311–8. doi: 10.1038/ng.3926 28759005

17. 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(12):1102–10. doi: 10.1038/nbt.2749 24270849

18. Benjamini Yoav HY. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;72(4):405–16.

19. Hemani G, Zheng J, Wade KH, Laurin C, Elsworth B, Burgess S, et al. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv 078972 [preprint]. 2016; https://doi.org/10.1101/078972.

20. Hemani G, Bowden J, Haycock P, Zheng J, Davis O, Flach P, et al. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. bioRxiv 173682 [preprint]. 2017; https://doi.org/10.1101/173682.

21. Feig DI, Soletsky B, Johnson RJ. Effect of allopurinol on blood pressure of adolescents with newly diagnosed essential hypertension: a randomized trial. JAMA. 2008;300:924–32. doi: 10.1001/jama.300.8.924 18728266

22. Wang J, Qin T, Chen J, Li Y, Wang L, Huang H, et al. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PLoS ONE. 2014;9(12):e114259. doi: 10.1371/journal.pone.0114259 25437867

23. Billiet L, Doaty S, Katz JD, Velasquez MT. Review of hyperuricemia as new marker for metabolic syndrome. ISRN Rheumatol. 2014;2014:1–7.

24. Peng TC, Wang CC, Kao TW, Chan JY, Yang YH, Chang YW, et al. Relationship between hyperuricemia and lipid profiles in US adults. Biomed Res Int. 2015;2015:1–7.

25. Li C, Hsieh MC, Chang SJ. Metabolic syndrome, diabetes, and hyperuricemia. Curr Opin Rheumatol. 2013;25(2):210–6. doi: 10.1097/BOR.0b013e32835d951e 23370374

26. Raimondo A, Rees MG, Gloyn AL. Glucokinase regulatory protein: complexity at the crossroads of triglyceride and glucose metabolism. Curr Opin Lipidol. 2015;26(2):88–95. doi: 10.1097/MOL.0000000000000155 25692341

27. Bi M, Kao WH, Boerwinkle E, Hoogeveen RC, Rasmussen-Torvik LJ, Astor BC, et al. Association of rs780094 in GCKR with metabolic traits and incident diabetes and cardiovascular disease: the ARIC Study. PLoS ONE. 2010; 5(7), e11690. doi: 10.1371/journal.pone.0011690 20661421

28. Mazharian A, Mori J, Wang YJ, Heising S, Neel BG, Watson SP, et al. Megakaryocyte-specific deletion of the protein-tyrosine phosphatases Shp1 and Shp2 causes abnormal megakaryocyte development, platelet production, and function. Blood. 2013;121(20):4205–20. doi: 10.1182/blood-2012-08-449272 23509158

29. Combe B, Landewe R, Daien CI, Hua C, Aletaha D, Alvaro-Gracia JM, et al. 2016 update of the EULAR recommendations for the management of early arthritis. Ann Rheum Dis. 2017;76(6):948–59. doi: 10.1136/annrheumdis-2016-210602 27979873