Abstract: Aims/hypothesis: Our aim was to investigate the relationship between average blood glucose levels and incident CHD in individuals without diabetes mellitus. Methods: To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to be associated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed to investigate associations with incident CHD risk in 324,830 European ancestry individuals from the UK Biobank without diabetes mellitus. Results: Every one mmol/mol increase in genetically proxied HbA1c was associated with an 11% higher CHD risk (HR 1.11, 95% CI 1.05, 1.18). The dose–response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linear relationship over a linear one. Conclusions/interpretations: In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHD risk in a dose-dependent way. Graphical abstract

Burgess, Stephen

Dichgans, Martin

Georgakis, Marios K.

Gill, Dipender

Liu, Bowen

Malik, Rainer

Mason, Amy M.

Diabetologia

Apollo (Cambridge)

Dose–response relationship between genetically proxied average blood glucose levels and incident coronary heart disease in individuals without diabetes mellitus

AIMS/HYPOTHESIS: Our aim was to investigate the relationship between average blood glucose levels and incident CHD in individuals without diabetes mellitus. METHODS: To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to be associated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed to investigate associations with incident CHD risk in 324,830 European ancestry individuals from the UK Biobank without diabetes mellitus. RESULTS: Every one mmol/mol increase in genetically proxied HbA1c was associated with an 11% higher CHD risk (HR 1.11, 95% CI 1.05, 1.18). The dose-response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linear relationship over a linear one. CONCLUSIONS/INTERPRETATIONS: In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHD risk in a dose-dependent way

Burgess, S

Malik, R

Liu, B

Mason, AM

Georgakis, MK

Dichgans, M

Gill, D

St George's Online Research Archive

SHORT COMMUNICATIONDose–response relationship between genetically proxiedaverage blood glucose levels and incident coronary heart diseasein individuals without diabetes mellitusStephen Burgess1,2 & Rainer Malik3 & Bowen Liu2 & Amy M. Mason2 & Marios K. Georgakis3 &Martin Dichgans3,4,5 & Dipender Gill6,7,8,9Received: 25 September 2020 /Accepted: 19 November 2020# The Author(s) 2021AbstractAims/hypothesis Our aim was to investigate the relationship between average blood glucose levels and incident CHD inindividuals without diabetes mellitus.Methods To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to beassociated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed toinvestigate associations with incident CHD risk in 324,830 European ancestry individuals from the UKBiobank without diabetesmellitus.Results Every one mmol/mol increase in genetically proxied HbA1c was associatedwith an 11% higher CHD risk (HR 1.11, 95%CI 1.05, 1.18). The dose–response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linearrelationship over a linear one.Conclusions/interpretations In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHDrisk in a dose-dependent way.Keywords Average blood glucose levels . CHD .Mendelian randomisationIntroductionCHD is a leading cause of mortality, accounting for approxi-mately 9 million deaths globally in 2015 alone [1].Epidemiological studies have supported an associationbetween average blood glucose levels and CHD risk, even inindividuals without diabetes mellitus [2]. However, it is diffi-cult to infer causal effects from observational studies becauseof the possibility that any identified associations may beattributable to confounding. Mendelian randomisation canovercome these limitations by using genetic variants as instru-mental variables to infer the effect of modifying an exposuresuch as average blood glucose levels on an outcome such asCHD. The aim of this study was to perform linear and non-linear Mendelian randomisation analyses to investigate theshape of the causal relationship between average bloodglucose levels (measured by HbA1c) and CHD risk in individ-uals without diabetes mellitus. HbA1c was considered* Dipender Gilldgill@sgul.ac.uk1 Medical Research Council Biostatistics Unit, Cambridge Institute ofPublic Health, Cambridge, UK2 Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK3 Institute for Stroke and Dementia Research, University Hospital ofLudwig-Maximilians-University, Munich, Germany4 Munich Cluster for Systems Neurology, Munich, Germany5 German Centre for Neurodegenerative Diseases, Munich, Germany6 Clinical Pharmacology and Therapeutics Section, Institute ofMedical and Biomedical Education and Institute for Infection andImmunity, St George’s, University of London, London, UK7 Clinical Pharmacology Group, Pharmacy andMedicines Directorate,St George’s University Hospitals NHS Foundation Trust,London, UK8 Novo Nordisk Research Centre Oxford, Old Road Campus,Oxford, UK9 Department of Epidemiology and Biostatistics, School of PublicHealth, Imperial College London, London, UKDiabetologiahttps://doi.org/10.1007/s00125-020-05377-0preferable to blood glucose levels because it represents theaverage blood glucose level over approximately 120 days,so is less susceptible to variation related to the time ofrecording.MethodsAnalyses were performed in unrelated participants ofEuropean ancestry from the UK Biobank, a population-based cohort study of middle-aged UK residents. Individualshaving possible diabetes mellitus (defined based on self-reporting, hospital episode statistics and medications) or base-line HbA1c > 47.5 mmol/mol (6.5%) were excluded. HbA1cwas measured in packed red blood cells using the Bio-RadVariant II Turbo analyser. International Classification ofDiseases 9th Revision (ICD-9) codes 410-414, and ICD-10codes I20-I25 were used to identify incident CHD cases. Fulldetails are provided in the electronic supplementary material(ESM).Candidate instrumental variablesWe selected 40 uncorrelated(r2 < 0.001) single-nucleotide polymorphisms as instrumentalvariables for average blood glucose levels based on their asso-ciation with type 2 diabetes (p < 5 × 10−8) in a genome-wideassociation study of 228,499 cases and 1,178,783 controls(79% European ancestry) that included UK Biobank partici-pants [3], and their association with HbA1c (p < 0.001 andconcordant direction of association) in an independent studyof 100,880 European ancestry participants (no overlap withthe UK Biobank) that were free of diabetes mellitus (asdefined by physician diagnosis, medications, or fastingglucose ≥7mmol/l) [4] (ESMTable 1).We created a weightedallele score for each participant by multiplying each type 2diabetes risk-increasing allele dosage with the variant’s asso-ciation with HbA1c, and summing across all 40 variants.Selecting variants associated with type 2 diabetes andweighting by their association with HbA1c helps ensure thatthe weighted allele score is reflective of average blood glucoselevels, rather than solely type 2 diabetes risk or HbA1c.Statistical analysis Mendelian randomisation analyses wereperformed to investigate the association between geneticallyproxied average blood glucose levels (measured as HbA1c)and incident CHD. Analyses were performed by modelling alinear relationship between genetically proxied average bloodglucose levels and incident CHD (‘linear Mendelianrandomisation’) [5], and also using the fractional polynomialmethod to test for a non-linear relationship between genetical-ly proxied average blood glucose levels and incident CHD(‘non-linear Mendelian randomisation’) [6]. We furtherassessed associations of the allele score with lipid fractionsand other glycaemic traits, and performed multivariable non-linear Mendelian randomisation for traits associated with theallele score that may represent alternative causal risk factors.As a further sensitivity analysis, we also performedMendelianrandomisation analysis that excluded variants associated withthe alternative causal risk factor at p < 0.01. Full details areprovided in the ESM. All statistical analysis was performedusing R (version 3.6.2) and only publicly available data fromstudies that had obtained relevant ethical approval and partic-ipant consent were used.DiabetologiaResultsBaseline characteristics for the 324,830 participants includedin the analyses are provided in ESMTable 2. There were 6006incident CHD events. The allele score explained 1.8% of thevariance in HbA1c, corresponding to an F-statistic of 144.5and a low risk of substantial weak instrument bias. For UKBiobank participants, associations of the variants incorporatedin the allele score with HbA1c were generally of greatermagnitude in men compared with women (ESM Fig. 1).Linear Mendelian randomisation Linear Mendelianrandomisation analyses identified a positive association betweenhigher genetically proxied average blood glucose levels and inci-dent CHD riskwhen consideringmen andwomen together (ESMFig. 2). For a one mmol/mol increase in HbA1c, the HR forincident CHD was 1.11 (95% CI 1.05, 1.18; p= 2 × 10−4). Insex-stratified analyses, the association was stronger in men (HR1.12, 95% CI 1.05, 1.19; p= 4 × 10−4) than in women (HR 1.08,95% CI 0.96, 1.20; p= 0.20) (ESM Table 3). Similar point esti-mates were obtained in sensitivity analyses using alternativeMendelian randomisation methods (ESM Table 4).Non-linear Mendelian randomisation In non-linear Mendelianrandomisation, we observed no statistical evidence favouring anon-linear relationship between genetically proxied HbA1c andincident CHD over a linear one in any of the analyses (Fig. 1).Subgroup analyses presenting Mendelian randomisation esti-mates in quintiles of the population based on HbA1c levels(corrected for genetic predisposition) are presented in ESMTable 3.Fractional polynomial: p = 0.97 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDFractional polynomial: p = 0.99 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDFractional polynomial: p = 0.99 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDa bcFig. 1 Non-linear Mendelian randomisation investigating the relation-ship between genetically proxied average blood glucose levels (measuredby HbA1c) and risk of incident CHD in individuals without diabetesmellitus: (a) men and women combined; (b) men only; and (c) womenonly. The x-axis depicts HbA1c levels inmmol/mol. The y-axis depicts thehazard ratio for coronary heart disease (HR for CHD) with respect to thereference, plotted on a log scale. Reference is set to anHbA1c of 30mmol/mol (4.9%). The grey lines represent the 95% CIs. The fractional poly-nomial test is a goodness-of-fit test that assesses whether any improve-ment of fit when using a non-linear function to model the association,compared with a linear function, is greater than would be expected due tochance (a significant p value indicates that a non-linear model is preferredto a linear model)DiabetologiaWe assessed genetic associations of the allele score and itsindividual variants with other glycaemic traits and lipid fractions(ESM Table 5 and ESM Fig. 3). The allele score was associatedwith 2-h glucose (p< 0.001) and fasting glucose (p< 0.001), butnot fasting insulin (p= 0.22). The allele score was also associatedwith LDL-cholesterol (LDL-cholesterol, p= 0.03), but not HDL-cholesterol (p = 0.86) or triacylglycerols (p = 0.99). Althoughmultivariable non-linear Mendelian randomisation adjusting forgenetically proxied LDL-cholesterol showed some attenuation inthe coefficient for genetically proxied HbA1c, an associationpersisted (HR 1.07, 95% CI 1.01, 1.14; p= 0.018), and the best-fitting fractional polynomial was the linear model (ESM Fig. 4).Similar results were also obtained after excluding the five variantsthat associated with LDL-cholesterol at p < 0.01 (rs1260326,rs10184004, rs11708067, rs505922 and rs174541): HR 1.10(95% CI 1.03, 1.17; p= 0.003) (ESM Fig. 5).DiscussionIn this Mendelian randomisation study, we found geneticevidence supporting an effect of higher average blood glucoselevels on increasing CHD risk in individuals without diabetesmellitus. We did not find evidence favouring a non-linearrelationship between HbA1c and CHD risk over a linear one.Our findings provide evidence that lowering average bloodglucose levels in individuals without diabetes mellitus canreduce cardiovascular risk in a dose-dependent way.While pre-diabetes is known to increase cardiovascular risk [7],our work goes further to support a continuous effect of averageblood glucose levels that are within the ‘physiologically normal’HbA1c range. Our findings build on existing epidemiologicalresearch supporting an association between average blood glucoselevels andCVD risk in individualswithout diabetesmellitus [2].ApreviousMendelian randomisation study similarly showed a posi-tive association between genetically proxiedHbA1c andCHD risk,although with wider CIs than in our current work, likely becausethat work used fewer genetic variants as instrumental variables [8].Clinical trials have found that intensive lowering of HbA1c levelsin high-risk patients with type 2 diabetes does not confer a bene-ficial effect on cardiovascular risk [9, 10]. This discrepancy maybe related to the particular pharmacological treatments used tolower HbA1c levels, including adverse effects such as weight gain,hypoglycaemia and rapid fluctuations in glucose levels [9, 10].Pharmacological agents for blood glucose lowering that are notassociated with weight gain or hypoglycaemia are available [11].Clinical trials are warranted to explore whether particular bloodglucose lowering strategies can be used to reduce cardiovascularrisk in patients without diabetes mellitus.A strength of our study is that the employed Mendelianrandomisation approach is robust to confounding from environ-mental factors. The weights for the variants used in Mendelianrandomisation analysis were derived from their associationswith HbA1c in a dataset that did not include the UK Biobank[4]. Furthermore, analysis was restricted to UK Biobank partic-ipants free of type 2 diabetes, thus avoiding any influence ofwinner’s curse bias. We further performed sensitivity analysesthat accounted for potential genetic confounding through LDL-C, which also identified associations of genetically proxiedHbA1c levels with incident CHD risk. However, our study alsohas limitations. The analyses were restricted to UK Biobankparticipants of European ancestry, and may not apply to otherpopulations. Furthermore, the genetic variants employed asinstrumental variables only explained 1.8% of the variance inHbA1c, thus limiting the statistical power of the analyses andthe precision of the results. A reason why the Mendelianrandomisation estimates were less precise in women comparedwith men may be that the genetic variants had weaker associa-tions with HbA1c in women. Finally, the genetic variantsemployed as instruments in this analysis proxy lifelong averageblood glucose control, and therefore cannot be used to informon the quantitative effects of discrete clinical interventions thatlower blood glucose levels in the short term.In summary, this Mendelian randomisation analysisprovides genetic evidence supporting an effect of averageblood glucose levels on CHD risk in individuals withoutdiabetes mellitus. Further work is required to investigatewhether strategies that lower blood glucose levels can reducecardiovascular risk in individuals without diabetes mellitus.Supplementary Information The online version of this article (https://doi.org/10.1007/s00125-020-05377-0) contains peer-reviewed but uneditedsupplementary material.Data availability This research was performed using UK Biobank data(application 29202), which is available on request (https://www.ukbiobank.ac.uk/register-apply). The genetic variants used asinstrumental variables are detailed in ESM Table 1.Funding SB is supported by a Sir Henry Dale Fellowship jointly fundedby the Wellcome Trust and the Royal Society (204623/Z/16/Z). MKG isfunded by a scholarship from the Onassis Foundation. MD acknowledgesfunding from the European Union’s Horizon 2020 research and innova-tion programme (666881), SVDs@target (667375), CoSTREAM,SyNergy (EXC 2145 SyNergy – ID 390857198), the CRC 1123 (B3and project DI 722/13-1), the Corona Foundation, the LMUexcellentfond, the e:Med programme (e:AtheroSysMed) and the FP7/2007-2103EuropeanUnion project CVgenes@target (Health-F2-2013-601456). DGis supported by the British Heart Foundation Centre of ResearchExcellence (RE/18/4/34215) at Imperial College London. This workwas supported by funding from the National Institute for HealthResearch (NIHR; Cambridge Biomedical Research Centre at theCambridge University Hospitals NHS Foundation Trust). The viewsexpressed are those of the authors and not necessarily those of theNHS, the NIHR or the Department of Health and Social Care.Authors’ relationships and activities DG is employed part-time by NovoNordisk. The remaining authors have no conflicts of interest to declare.Contribution statement DG and SB designed the study. SB, AMM andDG performed analysis. DG and SB drafted the manuscript. All authorsDiabetologiainterpreted the results and critically revised the manuscript for intellectualcontent. DG takes responsibility for the contents of the article. All authorsapproved the final version of this manuscript to be published.Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which permits use, sharing, adap-tation, distribution and reproduction in any medium or format, as long asyou give appropriate credit to the original author(s) and the source,provide a link to the Creative Commons licence, and indicate if changeswere made. The images or other third party material in this article areincluded in the article's Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included inthe article's Creative Commons licence and your intended use is notpermitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view acopy of this licence, visit http://creativecommons.org/licenses/by/4.0/.References1. Roth GA, Johnson C, Abajobir A et al (2017) Global, regional, andnational burden of cardiovascular diseases for 10 causes, 1990 to2015. J Am Coll Cardiol 70(1):1–25. https://doi.org/10.1016/j.jacc.2017.04.0522. Levitan EB, Song Y, Ford ES et al (2004) Is nondiabetic hypergly-cemia a risk factor for cardiovascular disease? A meta-analysis ofprospective studies. Arch Intern Med 164(19):2147–2155. https://doi.org/10.1001/archinte.164.19.21473. Vujkovic M, Keaton JM, Lynch JA et al (2020) Discovery of 318new risk loci for type 2 diabetes and related vascular outcomesamong 1.4 million participants in a multi-ancestry meta-analysis.Nat Genet 52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y4. Wheeler E, Leong A, Liu CT et al (2017) Impact of common genet-ic determinants of hemoglobin A1c on type 2 diabetes risk anddiagnosis in ancestrally diverse populations: a transethnicgenome-wide meta-analysis. PLoS Med 14(9):e1002383. https://doi.org/10.1371/journal.pmed.10023835. Burgess S, Dudbridge F, Thompson SG (2016) Combining infor-mation on multiple instrumental variables in Mendelian randomi-zation: comparison of allele score and summarized data methods.Stat Med 35(11):1880–1906. https://doi.org/10.1002/sim.68356. Burgess S, Davies NM, Thompson SG et al (2014) Instrumentalvariable analysis with a nonlinear exposure-outcome relationship.Epidemiology 25(6):877–885. https://doi.org/10.1097/EDE.00000000000001617. Cai X, Zhang Y, Li M et al (2020) Association between prediabetesand risk of all cause mortality and cardiovascular disease: updatedmeta-analysis. BMJ 370:m2297. https://doi.org/10.1136/bmj.m22978. Ahmad OS, Morris JA, Mujammami M et al (2015) A Mendelianrandomization study of the effect of type-2 diabetes on coronaryheart disease. Nat Commun 6:7060. https://doi.org/10.1038/ncomms80609. Action to Control Cardiovascular Risk in Diabetes Study Group,Gerstein HC, Miller ME et al (2008) Effects of intensive glucoselowering in type 2 diabetes. N Engl J Med 358(24):2545–2559.https://doi.org/10.1056/NEJMoa080274310. Advance Collaborative Group, Patel A, MacMahon S et al (2008)Intensive blood glucose control and vascular outcomes in patientswith type 2 diabetes. N Engl JMed 358(24):2560–2572. https://doi.org/10.1056/NEJMoa080298711. Kristensen SL, Rorth R, Jhund PS et al (2019) Cardiovascular,mortality, and kidney outcomes with GLP-1 receptor agonists inpatients with type 2 diabetes: a systematic review andmeta-analysisof cardiovascular outcome trials. Lancet Diabetes Endocrinol 7(10):776–785. https://doi.org/10.1016/S2213-8587(19)30249-9Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.Diabetologia

Dose-response relationship between genetically proxied average blood glucose levels and incident coronary heart disease in individuals without diabetes mellitus.

Aims/hypothesis Our aim was to investigate the relationship between average blood glucose levels and incident CHD in individuals without diabetes mellitus. Methods To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to be associated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed to investigate associations with incident CHD risk in 324,830 European ancestry individuals from the UK Biobank without diabetes mellitus. Results Every one mmol/mol increase in genetically proxied HbA1c was associated with an 11% higher CHD risk (HR 1.11, 95% CI 1.05, 1.18). The dose–response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linear relationship over a linear one. Conclusions/interpretations In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHD risk in a dose-dependent way

Spiral - Imperial College Digital Repository

SHORT COMMUNICATIONDose–response relationship between genetically proxiedaverage blood glucose levels and incident coronary heart diseasein individuals without diabetes mellitusStephen Burgess1,2 & Rainer Malik3 & Bowen Liu2 & Amy M. Mason2 & Marios K. Georgakis3 &Martin Dichgans3,4,5 & Dipender Gill6,7,8,9Received: 25 September 2020 /Accepted: 19 November 2020# The Author(s) 2021AbstractAims/hypothesis Our aim was to investigate the relationship between average blood glucose levels and incident CHD inindividuals without diabetes mellitus.Methods To investigate average blood glucose levels, we studied HbA1c as predicted by 40 variants previously shown to beassociated with both type 2 diabetes and HbA1c. Linear and non-linear Mendelian randomisation analyses were performed toinvestigate associations with incident CHD risk in 324,830 European ancestry individuals from the UKBiobank without diabetesmellitus.Results Every one mmol/mol increase in genetically proxied HbA1c was associatedwith an 11% higher CHD risk (HR 1.11, 95%CI 1.05, 1.18). The dose–response curve increased at all levels of HbA1c, and there was no evidence favouring a non-linearrelationship over a linear one.Conclusions/interpretations In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHDrisk in a dose-dependent way.Keywords Average blood glucose levels . CHD .Mendelian randomisationIntroductionCHD is a leading cause of mortality, accounting for approxi-mately 9 million deaths globally in 2015 alone [1].Epidemiological studies have supported an associationbetween average blood glucose levels and CHD risk, even inindividuals without diabetes mellitus [2]. However, it is diffi-cult to infer causal effects from observational studies becauseof the possibility that any identified associations may beattributable to confounding. Mendelian randomisation canovercome these limitations by using genetic variants as instru-mental variables to infer the effect of modifying an exposuresuch as average blood glucose levels on an outcome such asCHD. The aim of this study was to perform linear and non-linear Mendelian randomisation analyses to investigate theshape of the causal relationship between average bloodglucose levels (measured by HbA1c) and CHD risk in individ-uals without diabetes mellitus. HbA1c was considered* Dipender Gilldgill@sgul.ac.uk1 Medical Research Council Biostatistics Unit, Cambridge Institute ofPublic Health, Cambridge, UK2 Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK3 Institute for Stroke and Dementia Research, University Hospital ofLudwig-Maximilians-University, Munich, Germany4 Munich Cluster for Systems Neurology, Munich, Germany5 German Centre for Neurodegenerative Diseases, Munich, Germany6 Clinical Pharmacology and Therapeutics Section, Institute ofMedical and Biomedical Education and Institute for Infection andImmunity, St George’s, University of London, London, UK7 Clinical Pharmacology Group, Pharmacy andMedicines Directorate,St George’s University Hospitals NHS Foundation Trust,London, UK8 Novo Nordisk Research Centre Oxford, Old Road Campus,Oxford, UK9 Department of Epidemiology and Biostatistics, School of PublicHealth, Imperial College London, London, UKhttps://doi.org/10.1007/s00125-020-05377-0/ Published online: 26 January 2021Diabetologia (2021) 64:845–849preferable to blood glucose levels because it represents theaverage blood glucose level over approximately 120 days,so is less susceptible to variation related to the time ofrecording.MethodsAnalyses were performed in unrelated participants ofEuropean ancestry from the UK Biobank, a population-based cohort study of middle-aged UK residents. Individualshaving possible diabetes mellitus (defined based on self-reporting, hospital episode statistics and medications) or base-line HbA1c > 47.5 mmol/mol (6.5%) were excluded. HbA1cwas measured in packed red blood cells using the Bio-RadVariant II Turbo analyser. International Classification ofDiseases 9th Revision (ICD-9) codes 410-414, and ICD-10codes I20-I25 were used to identify incident CHD cases. Fulldetails are provided in the electronic supplementary material(ESM).Candidate instrumental variablesWe selected 40 uncorrelated(r2 < 0.001) single-nucleotide polymorphisms as instrumentalvariables for average blood glucose levels based on their asso-ciation with type 2 diabetes (p < 5 × 10−8) in a genome-wideassociation study of 228,499 cases and 1,178,783 controls(79% European ancestry) that included UK Biobank partici-pants [3], and their association with HbA1c (p < 0.001 andconcordant direction of association) in an independent studyof 100,880 European ancestry participants (no overlap withthe UK Biobank) that were free of diabetes mellitus (asdefined by physician diagnosis, medications, or fastingglucose ≥7mmol/l) [4] (ESMTable 1).We created a weightedallele score for each participant by multiplying each type 2diabetes risk-increasing allele dosage with the variant’s asso-ciation with HbA1c, and summing across all 40 variants.Selecting variants associated with type 2 diabetes andweighting by their association with HbA1c helps ensure thatthe weighted allele score is reflective of average blood glucoselevels, rather than solely type 2 diabetes risk or HbA1c.Statistical analysis Mendelian randomisation analyses wereperformed to investigate the association between geneticallyproxied average blood glucose levels (measured as HbA1c)and incident CHD. Analyses were performed by modelling alinear relationship between genetically proxied average bloodglucose levels and incident CHD (‘linear Mendelianrandomisation’) [5], and also using the fractional polynomialmethod to test for a non-linear relationship between genetical-ly proxied average blood glucose levels and incident CHD(‘non-linear Mendelian randomisation’) [6]. We furtherassessed associations of the allele score with lipid fractionsand other glycaemic traits, and performed multivariable non-linear Mendelian randomisation for traits associated with theallele score that may represent alternative causal risk factors.As a further sensitivity analysis, we also performedMendelianrandomisation analysis that excluded variants associated withthe alternative causal risk factor at p < 0.01. Full details areprovided in the ESM. All statistical analysis was performedusing R (version 3.6.2) and only publicly available data fromstudies that had obtained relevant ethical approval and partic-ipant consent were used.846 Diabetologia (2021) 64:845–849ResultsBaseline characteristics for the 324,830 participants includedin the analyses are provided in ESMTable 2. There were 6006incident CHD events. The allele score explained 1.8% of thevariance in HbA1c, corresponding to an F-statistic of 144.5and a low risk of substantial weak instrument bias. For UKBiobank participants, associations of the variants incorporatedin the allele score with HbA1c were generally of greatermagnitude in men compared with women (ESM Fig. 1).Linear Mendelian randomisation Linear Mendelianrandomisation analyses identified a positive association betweenhigher genetically proxied average blood glucose levels and inci-dent CHD riskwhen consideringmen andwomen together (ESMFig. 2). For a one mmol/mol increase in HbA1c, the HR forincident CHD was 1.11 (95% CI 1.05, 1.18; p= 2 × 10−4). Insex-stratified analyses, the association was stronger in men (HR1.12, 95% CI 1.05, 1.19; p= 4 × 10−4) than in women (HR 1.08,95% CI 0.96, 1.20; p= 0.20) (ESM Table 3). Similar point esti-mates were obtained in sensitivity analyses using alternativeMendelian randomisation methods (ESM Table 4).Non-linear Mendelian randomisation In non-linear Mendelianrandomisation, we observed no statistical evidence favouring anon-linear relationship between genetically proxied HbA1c andincident CHD over a linear one in any of the analyses (Fig. 1).Subgroup analyses presenting Mendelian randomisation esti-mates in quintiles of the population based on HbA1c levels(corrected for genetic predisposition) are presented in ESMTable 3.Fractional polynomial: p = 0.97 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDFractional polynomial: p = 0.99 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDFractional polynomial: p = 0.99 0.61.01.52.03.04.05.030 35 40HbA1c (mmol/mol)HR for CHDa bcFig. 1 Non-linear Mendelian randomisation investigating the relation-ship between genetically proxied average blood glucose levels (measuredby HbA1c) and risk of incident CHD in individuals without diabetesmellitus: (a) men and women combined; (b) men only; and (c) womenonly. The x-axis depicts HbA1c levels inmmol/mol. The y-axis depicts thehazard ratio for coronary heart disease (HR for CHD) with respect to thereference, plotted on a log scale. Reference is set to anHbA1c of 30mmol/mol (4.9%). The grey lines represent the 95% CIs. The fractional poly-nomial test is a goodness-of-fit test that assesses whether any improve-ment of fit when using a non-linear function to model the association,compared with a linear function, is greater than would be expected due tochance (a significant p value indicates that a non-linear model is preferredto a linear model)847Diabetologia (2021) 64:845–849We assessed genetic associations of the allele score and itsindividual variants with other glycaemic traits and lipid fractions(ESM Table 5 and ESM Fig. 3). The allele score was associatedwith 2-h glucose (p< 0.001) and fasting glucose (p< 0.001), butnot fasting insulin (p= 0.22). The allele score was also associatedwith LDL-cholesterol (LDL-cholesterol, p= 0.03), but not HDL-cholesterol (p = 0.86) or triacylglycerols (p = 0.99). Althoughmultivariable non-linear Mendelian randomisation adjusting forgenetically proxied LDL-cholesterol showed some attenuation inthe coefficient for genetically proxied HbA1c, an associationpersisted (HR 1.07, 95% CI 1.01, 1.14; p= 0.018), and the best-fitting fractional polynomial was the linear model (ESM Fig. 4).Similar results were also obtained after excluding the five variantsthat associated with LDL-cholesterol at p < 0.01 (rs1260326,rs10184004, rs11708067, rs505922 and rs174541): HR 1.10(95% CI 1.03, 1.17; p= 0.003) (ESM Fig. 5).DiscussionIn this Mendelian randomisation study, we found geneticevidence supporting an effect of higher average blood glucoselevels on increasing CHD risk in individuals without diabetesmellitus. We did not find evidence favouring a non-linearrelationship between HbA1c and CHD risk over a linear one.Our findings provide evidence that lowering average bloodglucose levels in individuals without diabetes mellitus canreduce cardiovascular risk in a dose-dependent way.While pre-diabetes is known to increase cardiovascular risk [7],our work goes further to support a continuous effect of averageblood glucose levels that are within the ‘physiologically normal’HbA1c range. Our findings build on existing epidemiologicalresearch supporting an association between average blood glucoselevels andCVD risk in individualswithout diabetesmellitus [2].ApreviousMendelian randomisation study similarly showed a posi-tive association between genetically proxiedHbA1c andCHD risk,although with wider CIs than in our current work, likely becausethat work used fewer genetic variants as instrumental variables [8].Clinical trials have found that intensive lowering of HbA1c levelsin high-risk patients with type 2 diabetes does not confer a bene-ficial effect on cardiovascular risk [9, 10]. This discrepancy maybe related to the particular pharmacological treatments used tolower HbA1c levels, including adverse effects such as weight gain,hypoglycaemia and rapid fluctuations in glucose levels [9, 10].Pharmacological agents for blood glucose lowering that are notassociated with weight gain or hypoglycaemia are available [11].Clinical trials are warranted to explore whether particular bloodglucose lowering strategies can be used to reduce cardiovascularrisk in patients without diabetes mellitus.A strength of our study is that the employed Mendelianrandomisation approach is robust to confounding from environ-mental factors. The weights for the variants used in Mendelianrandomisation analysis were derived from their associationswith HbA1c in a dataset that did not include the UK Biobank[4]. Furthermore, analysis was restricted to UK Biobank partic-ipants free of type 2 diabetes, thus avoiding any influence ofwinner’s curse bias. We further performed sensitivity analysesthat accounted for potential genetic confounding through LDL-C, which also identified associations of genetically proxiedHbA1c levels with incident CHD risk. However, our study alsohas limitations. The analyses were restricted to UK Biobankparticipants of European ancestry, and may not apply to otherpopulations. Furthermore, the genetic variants employed asinstrumental variables only explained 1.8% of the variance inHbA1c, thus limiting the statistical power of the analyses andthe precision of the results. A reason why the Mendelianrandomisation estimates were less precise in women comparedwith men may be that the genetic variants had weaker associa-tions with HbA1c in women. Finally, the genetic variantsemployed as instruments in this analysis proxy lifelong averageblood glucose control, and therefore cannot be used to informon the quantitative effects of discrete clinical interventions thatlower blood glucose levels in the short term.In summary, this Mendelian randomisation analysisprovides genetic evidence supporting an effect of averageblood glucose levels on CHD risk in individuals withoutdiabetes mellitus. Further work is required to investigatewhether strategies that lower blood glucose levels can reducecardiovascular risk in individuals without diabetes mellitus.Supplementary Information The online version of this article (https://doi.org/10.1007/s00125-020-05377-0) contains peer-reviewed but uneditedsupplementary material.Data availability This research was performed using UK Biobank data(application 29202), which is available on request (https://www.ukbiobank.ac.uk/register-apply). The genetic variants used asinstrumental variables are detailed in ESM Table 1.Funding SB is supported by a Sir Henry Dale Fellowship jointly fundedby the Wellcome Trust and the Royal Society (204623/Z/16/Z). MKG isfunded by a scholarship from the Onassis Foundation. MD acknowledgesfunding from the European Union’s Horizon 2020 research and innova-tion programme (666881), SVDs@target (667375), CoSTREAM,SyNergy (EXC 2145 SyNergy – ID 390857198), the CRC 1123 (B3and project DI 722/13-1), the Corona Foundation, the LMUexcellentfond, the e:Med programme (e:AtheroSysMed) and the FP7/2007-2103EuropeanUnion project CVgenes@target (Health-F2-2013-601456). DGis supported by the British Heart Foundation Centre of ResearchExcellence (RE/18/4/34215) at Imperial College London. This workwas supported by funding from the National Institute for HealthResearch (NIHR; Cambridge Biomedical Research Centre at theCambridge University Hospitals NHS Foundation Trust). The viewsexpressed are those of the authors and not necessarily those of theNHS, the NIHR or the Department of Health and Social Care.Authors’ relationships and activities DG is employed part-time by NovoNordisk. The remaining authors have no conflicts of interest to declare.Contribution statement DG and SB designed the study. SB, AMM andDG performed analysis. DG and SB drafted the manuscript. All authors848 Diabetologia (2021) 64:845–849interpreted the results and critically revised the manuscript for intellectualcontent. DG takes responsibility for the contents of the article. All authorsapproved the final version of this manuscript to be published.Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which permits use, sharing, adap-tation, distribution and reproduction in any medium or format, as long asyou give appropriate credit to the original author(s) and the source,provide a link to the Creative Commons licence, and indicate if changeswere made. The images or other third party material in this article areincluded in the article's Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included inthe article's Creative Commons licence and your intended use is notpermitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view acopy of this licence, visit http://creativecommons.org/licenses/by/4.0/.References1. Roth GA, Johnson C, Abajobir A et al (2017) Global, regional, andnational burden of cardiovascular diseases for 10 causes, 1990 to2015. J Am Coll Cardiol 70(1):1–25. https://doi.org/10.1016/j.jacc.2017.04.0522. Levitan EB, Song Y, Ford ES et al (2004) Is nondiabetic hypergly-cemia a risk factor for cardiovascular disease? A meta-analysis ofprospective studies. Arch Intern Med 164(19):2147–2155. https://doi.org/10.1001/archinte.164.19.21473. Vujkovic M, Keaton JM, Lynch JA et al (2020) Discovery of 318new risk loci for type 2 diabetes and related vascular outcomesamong 1.4 million participants in a multi-ancestry meta-analysis.Nat Genet 52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y4. Wheeler E, Leong A, Liu CT et al (2017) Impact of common genet-ic determinants of hemoglobin A1c on type 2 diabetes risk anddiagnosis in ancestrally diverse populations: a transethnicgenome-wide meta-analysis. PLoS Med 14(9):e1002383. https://doi.org/10.1371/journal.pmed.10023835. Burgess S, Dudbridge F, Thompson SG (2016) Combining infor-mation on multiple instrumental variables in Mendelian randomi-zation: comparison of allele score and summarized data methods.Stat Med 35(11):1880–1906. https://doi.org/10.1002/sim.68356. Burgess S, Davies NM, Thompson SG et al (2014) Instrumentalvariable analysis with a nonlinear exposure-outcome relationship.Epidemiology 25(6):877–885. https://doi.org/10.1097/EDE.00000000000001617. Cai X, Zhang Y, Li M et al (2020) Association between prediabetesand risk of all cause mortality and cardiovascular disease: updatedmeta-analysis. BMJ 370:m2297. https://doi.org/10.1136/bmj.m22978. Ahmad OS, Morris JA, Mujammami M et al (2015) A Mendelianrandomization study of the effect of type-2 diabetes on coronaryheart disease. Nat Commun 6:7060. https://doi.org/10.1038/ncomms80609. Action to Control Cardiovascular Risk in Diabetes Study Group,Gerstein HC, Miller ME et al (2008) Effects of intensive glucoselowering in type 2 diabetes. N Engl J Med 358(24):2545–2559.https://doi.org/10.1056/NEJMoa080274310. Advance Collaborative Group, Patel A, MacMahon S et al (2008)Intensive blood glucose control and vascular outcomes in patientswith type 2 diabetes. N Engl JMed 358(24):2560–2572. https://doi.org/10.1056/NEJMoa080298711. Kristensen SL, Rorth R, Jhund PS et al (2019) Cardiovascular,mortality, and kidney outcomes with GLP-1 receptor agonists inpatients with type 2 diabetes: a systematic review andmeta-analysisof cardiovascular outcome trials. Lancet Diabetes Endocrinol 7(10):776–785. https://doi.org/10.1016/S2213-8587(19)30249-9Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.849Diabetologia (2021) 64:845–849

Dose-response relationship between genetically proxied average blood glucose levels and incident coronary heart disease in individuals without diabetes mellitus

Mason, Amy M

Georgakis, Marios K

Aims/hypothesis Our aim was to investigate the relationship between average blood glucose levels and incident CHD in individuals without diabetes mellitus. Methods To investigate average blood glucose levels, we studied HbA(1c) as predicted by 40 variants previously shown to be associated with both type 2 diabetes and HbA(1c). Linear and non-linear Mendelian randomisation analyses were performed to investigate associations with incident CHD risk in 324,830 European ancestry individuals from the UK Biobank without diabetes mellitus. Results Every one mmol/mol increase in genetically proxied HbA(1c) was associated with an 11% higher CHD risk (HR 1.11, 95% CI 1.05, 1.18). The dose-response curve increased at all levels of HbA(1c), and there was no evidence favouring a non-linear relationship over a linear one. Conclusions/interpretations In individuals without diabetes mellitus, lowering average blood glucose levels may reduce CHD risk in a dose-dependent way

Open Access LMU

English

https://epub.ub.uni-muenchen.de/97300/1/s00125-020-05377-0_1_.pdf

Dose–response relationship between genetically proxied average blood glucose levels and incident coronary heart disease in individuals without diabetes mellitus

Abstract

Similar works

Full text

Available Versions

Apollo (Cambridge)

St George's Online Research Archive

Spiral - Imperial College Digital Repository

Apollo (Cambridge)

Open Access LMU