The association between circulating 25-hydroxyvitamin D metabolites and type 2 diabetes in European populations: A meta-analysis and Mendelian randomisation analysis
Autoři:
Ju-Sheng Zheng aff001; Jian’an Luan aff001; Eleni Sofianopoulou aff003; Stephen J. Sharp aff001; Felix R. Day aff001; Fumiaki Imamura aff001; Thomas E. Gundersen aff005; Luca A. Lotta aff001; Ivonne Sluijs aff006; Isobel D. Stewart aff001; Rupal L. Shah aff001; Yvonne T. van der Schouw aff006; Eleanor Wheeler aff001; Eva Ardanaz aff007; Heiner Boeing aff010; Miren Dorronsoro aff011; Christina C. Dahm aff012; Niki Dimou aff013; Douae El-Fatouhi aff014; Paul W. Franks aff015; Guy Fagherazzi aff014; Sara Grioni aff017; José María Huerta aff009; Alicia K. Heath aff019; Louise Hansen aff020; Mazda Jenab aff013; Paula Jakszyn aff021; Rudolf Kaaks aff023; Tilman Kühn aff023; Kay-Tee Khaw aff024; Nasser Laouali aff014; Giovanna Masala aff025; Peter M. Nilsson aff015; Kim Overvad aff012; Anja Olsen aff020; Salvatore Panico aff027; J. Ramón Quirós aff028; Olov Rolandsson aff029; Miguel Rodríguez-Barranco aff009; Carlotta Sacerdote aff032; Annemieke M. W. Spijkerman aff033; Tammy Y. N. Tong aff034; Rosario Tumino aff035; Konstantinos K. Tsilidis aff019; John Danesh aff003; Elio Riboli aff019; Adam S. Butterworth aff003; Claudia Langenberg aff001; Nita G. Forouhi aff001; Nicholas J. Wareham aff001
Působiště autorů:
MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
aff001; Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
aff002; MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
aff003; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
aff004; VITAS, Oslo, Norway
aff005; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
aff006; Navarra Public Health Institute, Pamplona, Spain
aff007; Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
aff008; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
aff009; Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Germany
aff010; Public Health Division of Gipuzkoa, San Sebastian, Spain
aff011; Department of Public Health, Aarhus University, Aarhus, Denmark
aff012; International Agency for Research on Cancer, Lyon, France
aff013; Center of Research in Epidemiology and Population Health, UMR 1018 Inserm, Institut Gustave Roussy, Paris South–Paris Saclay University, Villejuif, France
aff014; Department of Clinical Sciences, Lund University, Malmö, Sweden
aff015; Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
aff016; Epidemiology and Prevention Unit, Milan, Italy
aff017; Department of Epidemiology, Murcia Regional Health Council, Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca, Murcia, Spain
aff018; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
aff019; Danish Cancer Society Research Center, Copenhagen, Denmark
aff020; Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology–Institut d’Investigació Biomédica de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain
aff021; Facultat Ciències Salut Blanquerna, Universitat Ramon Llull, Barcelona, Spain
aff022; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
aff023; Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
aff024; Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
aff025; Department of Cardiology, Aalborg University Hospital, Aarhus, Denmark
aff026; Dipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II, Naples, Italy
aff027; Public Health Directorate, Asturias, Spain
aff028; Family Medicine Division, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
aff029; Andalusian School of Public Health (EASP), Granada, Spain
aff030; Instituto de Investigación Biosanitaria de Granada, Universidad de Granada, Granada, Spain
aff031; Unit of Cancer Epidemiology, Città della Salute e della Scienza di Torino University Hospital–University of Turin and Center for Cancer Prevention (CPO), Torino, Italy
aff032; National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
aff033; Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
aff034; Azienda Sanitaria Provinciale, Ragusa, Italy
aff035; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
aff036; British Heart Foundation Cambridge Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Cambridge, United Kingdom
aff037; Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
aff038
Vyšlo v časopise:
The association between circulating 25-hydroxyvitamin D metabolites and type 2 diabetes in European populations: A meta-analysis and Mendelian randomisation analysis. PLoS Med 17(10): e32767. doi:10.1371/journal.pmed.1003394
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003394
Souhrn
Background
Prior research suggested a differential association of 25-hydroxyvitamin D (25(OH)D) metabolites with type 2 diabetes (T2D), with total 25(OH)D and 25(OH)D3 inversely associated with T2D, but the epimeric form (C3-epi-25(OH)D3) positively associated with T2D. Whether or not these observational associations are causal remains uncertain. We aimed to examine the potential causality of these associations using Mendelian randomisation (MR) analysis.
Methods and findings
We performed a meta-analysis of genome-wide association studies for total 25(OH)D (N = 120,618), 25(OH)D3 (N = 40,562), and C3-epi-25(OH)D3 (N = 40,562) in participants of European descent (European Prospective Investigation into Cancer and Nutrition [EPIC]–InterAct study, EPIC-Norfolk study, EPIC-CVD study, Ely study, and the SUNLIGHT consortium). We identified genetic variants for MR analysis to investigate the causal association of the 25(OH)D metabolites with T2D (including 80,983 T2D cases and 842,909 non-cases). We also estimated the observational association of 25(OH)D metabolites with T2D by performing random effects meta-analysis of results from previous studies and results from the EPIC-InterAct study. We identified 10 genetic loci associated with total 25(OH)D, 7 loci associated with 25(OH)D3 and 3 loci associated with C3-epi-25(OH)D3. Based on the meta-analysis of observational studies, each 1–standard deviation (SD) higher level of 25(OH)D was associated with a 20% lower risk of T2D (relative risk [RR]: 0.80; 95% CI 0.77, 0.84; p < 0.001), but a genetically predicted 1-SD increase in 25(OH)D was not significantly associated with T2D (odds ratio [OR]: 0.96; 95% CI 0.89, 1.03; p = 0.23); this result was consistent across sensitivity analyses. In EPIC-InterAct, 25(OH)D3 (per 1-SD) was associated with a lower risk of T2D (RR: 0.81; 95% CI 0.77, 0.86; p < 0.001), while C3-epi-25(OH)D3 (above versus below lower limit of quantification) was positively associated with T2D (RR: 1.12; 95% CI 1.03, 1.22; p = 0.006), but neither 25(OH)D3 (OR: 0.97; 95% CI 0.93, 1.01; p = 0.14) nor C3-epi-25(OH)D3 (OR: 0.98; 95% CI 0.93, 1.04; p = 0.53) was causally associated with T2D risk in the MR analysis. Main limitations include the lack of a non-linear MR analysis and of the generalisability of the current findings from European populations to other populations of different ethnicities.
Conclusions
Our study found discordant associations of biochemically measured and genetically predicted differences in blood 25(OH)D with T2D risk. The findings based on MR analysis in a large sample of European ancestry do not support a causal association of total 25(OH)D or 25(OH)D metabolites with T2D and argue against the use of vitamin D supplementation for the prevention of T2D.
Klíčová slova:
Genetic loci – Genetics – Genome-wide association studies – Metaanalysis – Metabolites – Single nucleotide polymorphisms – Type 2 diabetes – vitamín D
Zdroje
1. Ye Z, Sharp SJ, Burgess S, Scott RA, Imamura F, InterAct Consortium, et al. Association between circulating 25-hydroxyvitamin D and incident type 2 diabetes: a Mendelian randomisation study. Lancet Diabetes Endocrinol. 2015;3:35–42. doi: 10.1016/S2213-8587(14)70184-6 25281353
2. Zheng J-S, Imamura F, Sharp SJ, van der Schouw YT, Sluijs I, Gundersen TE, et al. Association of plasma vitamin D metabolites with incident type 2 diabetes: EPIC-InterAct case-cohort study. J Clin Endocrinol Metab. 2019;104:1293–303. doi: 10.1210/jc.2018-01522 30418614
3. Avenell A, Cook JA, MacLennan GS, McPherson GC. Vitamin D supplementation and type 2 diabetes: a substudy of a randomised placebo-controlled trial in older people (RECORD trial, ISRCTN 51647438). Age Ageing. 2009;38(5):606–9. doi: 10.1093/ageing/afp109 19617604
4. De Boer IH, Tinker LF, Connelly S, Curb JD, Howard BV, Kestenbaum B, et al. Calcium plus vitamin D supplementation and the risk of incident diabetes in the women’s health initiative. Diabetes Care. 2008;31:701–7. doi: 10.2337/dc07-1829 18235052
5. Manson JE, Cook NR, Lee I-M, Christen W, Bassuk SS, Mora S, et al. Vitamin D supplements and prevention of cancer and cardiovascular disease. N Engl J Med. 2019;380:33–44. doi: 10.1056/NEJMoa1809944 30415629
6. Pittas AG, Dawson-Hughes B, Sheehan P, Ware JH, Knowler WC, Aroda VR, et al. Vitamin D supplementation and prevention of type 2 diabetes. N Engl J Med. 2019;380:23–32. doi: 10.1056/NEJMoa1811403 30415637
7. Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251–60. doi: 10.1093/aje/kwu283 25632051
8. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318:1925. doi: 10.1001/jama.2017.17219 29164242
9. Buijsse B, Boeing H, Hirche F, Weikert C, Schulze MB, Gottschald M, et al. Plasma 25-hydroxyvitamin D and its genetic determinants in relation to incident type 2 diabetes: a prospective case-cohort study. Eur J Epidemiol. 2013;28:743–52. doi: 10.1007/s10654-013-9844-5 24002339
10. Wang N, Wang C, Chen X, Wan H, Chen Y, Chen C, et al. Vitamin D, prediabetes and type 2 diabetes: bidirectional Mendelian randomization analysis. Eur J Nutr. 2019;59:1379–88. doi: 10.1007/s00394-019-01990-x 31076857
11. Afzal S, Brøndum-Jacobsen P, Bojesen SE, Nordestgaard BG. Vitamin D concentration, obesity, and risk of diabetes: a Mendelian randomisation study. Lancet Diabetes Endocrinol. 2014;2:298–306. doi: 10.1016/S2213-8587(13)70200-6 24703048
12. Lu L, Bennett DA, Millwood IY, Parish S, McCarthy MI, Mahajan A, et al. Association of vitamin D with risk of type 2 diabetes: a Mendelian randomisation study in European and Chinese adults. PLOS Med. 2018;15:e1002566. doi: 10.1371/journal.pmed.1002566 29718904
13. Wang TJ, Zhang F, Richards JB, Kestenbaum B, van Meurs JB, Berry D, et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet. 2010;376:180–8. doi: 10.1016/S0140-6736(10)60588-0 20541252
14. Bailey D, Veljkovic K, Yazdanpanah M, Adeli K. Analytical measurement and clinical relevance of vitamin D3C3-epimer. Clin Biochem. 2013;46(3):190–6. doi: 10.1016/j.clinbiochem.2012.10.037 23153571
15. Consortium InterAct, Langenberg C, Sharp S, Forouhi NG, Franks PW, Schulze MB, et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia. 2011;54:2272–82. doi: 10.1007/s00125-011-2182-9 21717116
16. Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A, et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer. 1999;80(Suppl 1):95–103.
17. Danesh J, Saracci R, Berglund G, Feskens E, Overvad K, Panico S, et al. EPIC-Heart: the cardiovascular component of a prospective study of nutritional, lifestyle and biological factors in 520,000 middle-aged participants from 10 European countries. Eur J Epidemiol. 2007;22:129–41. doi: 10.1007/s10654-006-9096-8 17295097
18. Forouhi NG, Luan J, Hennings S, Wareham NJ. Incidence of type 2 diabetes in England and its association with baseline impaired fasting glucose: the Ely study 1990–2000. Diabet Med. 2007;24:200–7. doi: 10.1111/j.1464-5491.2007.02068.x 17257284
19. Jiang X, O’Reilly PF, Aschard H, Hsu Y-H, Richards JB, Dupuis J, et al. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat Commun. 2018;9:260. doi: 10.1038/s41467-017-02662-2 29343764
20. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–13. doi: 10.1038/s41588-018-0241-6 30297969
21. Collins R. What makes UK Biobank special? Lancet. 2012;379:1173–4. doi: 10.1016/S0140-6736(12)60404-8 22463865
22. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1. doi: 10.1093/bioinformatics/btq340 20616382
23. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7. doi: 10.1093/bioinformatics/btq419 20634204
24. Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016;44:D877–81. doi: 10.1093/nar/gkv1340 26657631
25. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41. doi: 10.1038/ng.3406 26414676
26. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33:272–9. doi: 10.1093/bioinformatics/btw613 27663502
27. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206 doi: 10.1038/nature14177 25673413
28. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. doi: 10.1002/gepi.21965 27061298
29. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. doi: 10.1002/gepi.21758 24114802
30. 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. doi: 10.1038/s41588-018-0099-7 29686387
31. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat. 2020;3:1742–69. doi: 10.1214/19-AOS1866
32. Qi G, Chatterjee N. Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat Commun. 2019;10:1941. doi: 10.1038/s41467-019-09432-2 31028273
33. Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9. doi: 10.1093/bioinformatics/btw373 27318201
34. Song Y, Wang L, Pittas AG, Del Gobbo LC, Zhang C, Manson JE, et al. Blood 25-hydroxy vitamin D levels and incident type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care. 2013;36:1422–8. doi: 10.2337/dc12-0962 23613602
35. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi: 10.1136/bmj.k601 30002074
36. Grozdanov PN, Roy S, Kittur N, Meier UT. SHQ1 is required prior to NAF1 for assembly of H/ACA small nucleolar and telomerase RNPs. RNA. 2009;15:1188–97. doi: 10.1261/rna.1532109 19383767
37. Bikle DD. Vitamin D metabolism, mechanism of action, and clinical applications. Chem Biol. 2014;21(3):319–29. doi: 10.1016/j.chembiol.2013.12.016 24529992
38. Le Goff C, Cavalier E, Souberbielle JC, González-Antuña A, Delvin E. Measurement of circulating 25-hydroxyvitamin D: a historical review. Pract Lab Med. 2015;2:1–14. doi: 10.1016/j.plabm.2015.04.001 28932799
39. Burgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol. 2018;33:947–52. doi: 10.1007/s10654-018-0424-6 30039250
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