Plasma Vitamin C and Type 2 Diabetes: Genome-Wide Association Study and Mendelian Randomization Analysis in European Populations

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Abstract

OBJECTIVE

Higher plasma vitamin C levels are associated with lower type 2 diabetes risk, but whether this association is causal is uncertain. To investigate this, we studied the association of genetically predicted plasma vitamin C with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We conducted genome-wide association studies of plasma vitamin C among 52,018 individuals of European ancestry to discover novel genetic variants. We performed Mendelian randomization analyses to estimate the association of genetically predicted differences in plasma vitamin C with type 2 diabetes in up to 80,983 case participants and 842,909 noncase participants. We compared this estimate with the observational association between plasma vitamin C and incident type 2 diabetes, including 8,133 case participants and 11,073 noncase participants.

RESULTS

We identified 11 genomic regions associated with plasma vitamin C ( P < 5 × 10 ⁻⁸), with the strongest signal at SLC23A1, and 10 novel genetic loci including SLC23A3, CHPT1, BCAS3, SNRPF, RER1, MAF, GSTA5, RGS14, AKT1, and FADS1. Plasma vitamin C was inversely associated with type 2 diabetes (hazard ratio per SD 0.88; 95% CI 0.82, 0.94), but there was no association between genetically predicted plasma vitamin C (excluding FADS1 variant due to its apparent pleiotropic effect) and type 2 diabetes (1.03; 95% CI 0.96, 1.10).

CONCLUSIONS

These findings indicate discordance between biochemically measured and genetically predicted plasma vitamin C levels in the association with type 2 diabetes among European populations. The null Mendelian randomization findings provide no strong evidence to suggest the use of vitamin C supplementation for type 2 diabetes prevention.

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Most cited references 32

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

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Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

Stephen Burgess, Adam S. Butterworth, Simon Thompson (2013)

Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.

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Author and article information

Journal

Journal ID (nlm-ta): Diabetes Care

Journal ID (iso-abbrev): Diabetes Care

Journal ID (hwp): diacare

Journal ID (pmc): dcare

Journal ID (publisher-id): Diabetes Care

Title: Diabetes Care

Publisher: American Diabetes Association

ISSN (Print): 0149-5992

ISSN (Electronic): 1935-5548

Publication date (Print): January 2021

Publication date (Electronic): 17 November 2020

Publication date PMC-release: 17 November 2020

Volume: 44

Issue: 1

Pages: 98-106

Affiliations

[1] ¹Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, U.K.

[2] ²Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China

[3] ³British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K.

[4] ⁴Vitas Ltd, Oslo, Norway

[5] ⁵Ministry of Health of the Basque Government, Public Health Division of Gipuzkoa, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain

[6] ⁶Navarra Public Health Institute, Pamplona, Spain

[7] ⁷IdiSNA, Navarra Institute for Health Research, Pamplona, Spain

[8] ⁸CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain

[9] ⁹Department of Epidemiology, Regional Health Council, Instituto Murciano de Investigatión Biosanitaria (IMIB)-Arrixaca, Murcia University, Murcia, Spain

[10] ¹⁰Digital Epidemiology and e-Health Research Hub, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg, France

[11] ¹¹Center of Epidemiology and Population Health UMR 1018, INSERM, Paris South - Paris Saclay University, Gustave Roussy Institute, Villejuif, France

[12] ¹²Department of Clinical Sciences, Lund University, Malmö, Sweden

[13] ¹³Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

[14] ¹⁴Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

[15] ¹⁵Danish Cancer Society Research Center, Copenhagen, Denmark

[16] ¹⁶Department of Public Health, Aarhus University, Aarhus, Denmark

[17] ¹⁷Department of Cardiology, Aalborg University Hospital, Aarhus, Denmark

[18] ¹⁸Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy

[19] ¹⁹Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy

[20] ²⁰Department of Clinical and Biological Sciences, University of Turin, Orbassano, Turin, Italy

[21] ²¹Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco, Turin, Italy

[22] ²²Department of Public Health and Clinical Medicine, Family Medicine, Umeå University, Umeå, Sweden

[23] ²³National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

[24] ²⁴Andalusian School of Public Health, Granada, Spain

[25] ²⁵Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany

[26] ²⁶German Center for Diabetes Research (DZD), München-Neuherberg, Germany

[27] ²⁷Institute of Nutrition Science, University of Potsdam, Nuthetal, Germany

[28] ²⁸Unit of Nutrition and Cancer, Cancer Epidemiology Research Program and Translational Research Laboratory; Catalan Institute of Oncology - ICO, Group of Research on Nutrition and Cancer, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet of Llobregat, Barcelona, Spain

[29] ²⁹Epidemiology and Prevention Unit, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Nazionale dei Tumori di Milano Via Venezian, Milan, Italy

[30] ³⁰Institute of Public Health, University of Copenhagen, Copenhagen, Denmark

[31] ³¹Cancer Registry and Histopathology Department, Azienda Sanitaria Provinciale (ASP), Ragusa, Italy

[32] ³²International Agency for Research on Cancer, Lyon, France

[33] ³³School of Public Health, Imperial College, London, U.K.

[34] ³⁴National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, U.K.

[35] ³⁵British Heart Foundation Center of Research Excellence, University of Cambridge, Cambridge, U.K.

[36] ³⁶Department of Human Genetics, Wellcome Sanger Institute, Hinxton, U.K.

[37] ³⁷National Institute for Health Research Cambridge Biomedical Research Center, University of Cambridge and Cambridge University Hospitals, Cambridge, U.K.

[38] ³⁸Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, U.K.

Author notes

Corresponding authors: Nita G. Forouhi, nita.forouhi@ 123456mrc-epid.cam.ac.uk , and Nicholas J. Wareham, nick.wareham@ 123456mrc-epid.cam.ac.uk

Author information

Ju-Sheng Zheng https://orcid.org/0000-0001-6560-4890

Luca A. Lotta https://orcid.org/0000-0002-2619-5956

Guy Fagherazzi https://orcid.org/0000-0001-5033-5966

Paul W. Franks https://orcid.org/0000-0002-0520-7604

Olov Rolandsson https://orcid.org/0000-0002-1341-6828

Matthias B. Schulze https://orcid.org/0000-0002-0830-5277

Nita G. Forouhi https://orcid.org/0000-0002-5041-248X

Nicholas J. Wareham https://orcid.org/0000-0003-1422-2993

Article

Publisher ID: 201328

DOI: 10.2337/dc20-1328

PMC ID: 7783939

PubMed ID: 33203707

SO-VID: e5ef6cb1-5f94-41af-b153-82c9ba182737

License:

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.

History

Date received : 2 June 2020

Date accepted : 15 October 2020

Page count

Figures: 3, Tables: 1, Equations: 0, References: 32, Pages: 9

Funding

Funded by: European Union Sixth Framework Programme, DOI https://dx.doi.org/10.13039/100011103;

Award ID: LSHM_CT_2006_037197

Funded by: European Union Framework 7, DOI https://dx.doi.org/10.13039/100010677;

Award ID: HEALTH-F2-2012-279233

Funded by: British Heart Foundation, DOI https://dx.doi.org/10.13039/501100000274;

Award ID: SP/09/002

Award ID: RG/08/014

Award ID: RG13/13/30194

Funded by: Marie Skłodowska-Curie Actions, DOI https://dx.doi.org/10.13039/100010665;

Plasma Vitamin C and Type 2 Diabetes: Genome-Wide Association Study and Mendelian Randomization Analysis in European Populations

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Abstract

OBJECTIVE

RESEARCH DESIGN AND METHODS

RESULTS

CONCLUSIONS

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Journal of the ASEAN Federation of Endocrine Societies

Most cited references 32

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

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