spiral.imperial.ac.uk · Web viewCW Stewart PhD Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK Dr Ioanna Tzoulaki PhD Department pf Epidemiology and

HMG-CoA reductase inhibition, type 2 diabetes and body weight: evidence from genetic analysis and randomised trials

Title First name Init. Surname Preferred qualification(s) AffiliationDr Daniel I Swerdlow* MBBS, PhD UCL Institute of Cardiovascular Science, UCL, London, UKDr David Preiss* MD, PhD BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK

Ms Karoline B Kuchenbaecker Dipl.-Psych., MScCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Dr Michael V Holmes MD, PhD, MSc

Department of Surgery, Division of Transplantation, and Clinical Epidemiology Unit, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103UCL Institute of Cardiovascular Science, UCL, London, UK

Mr Jorgen EL Engmann MSc BSc Research Department of Epidemiology & Public Health, UCL, London, UKDr Tina Shah PhD Research Department of Epidemiology & Public Health, UCL, London, UKDr Reecha Sofat MD, PhD Research Department of Epidemiology & Public Health, UCL, London, UK

Dr Stefan Stender MD, PhDDepartment of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark

Dr Paul CD Johnson PhD Robertson Centre for Biostatistics, University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK

Dr Robert A Scott PhDMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK

Mr Maarten Leusink MScDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands

Mr Niek Verweij University of Groningen, University Medical Centre Groningen, Department of Cardiology, Groningen, the Netherlands

Mr Stephen J Sharp MScMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK

Dr Yiran Guo PhDCenter for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

Ms Claudia Giambartolomei MSc UCL Genetics Institute, UCL, London, UKMs Christina Chung MSc UCL Department of Epidemiology and Public HealthDr Anne Peasey PhD UCL Department of Epidemiology and Public HealthMs Antoinette Amuzu MSc London School of Hygiene & Tropical Medicine, Keppel Street, London, UK

Ms KaWah Li MScCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Ms Jutta Palmen MScCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Mr Philip Howard MScCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Ms Jackie A Cooper MScCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Dr Fotios Drenos PhDCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Ms Yun R Li BScCenter for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

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Professor Gordon Lowe DSc Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UKDr John Gallacher Department of Primary Care and Public Health, Cardiff University Medical School, Cardiff University, Cardiff, WalesDr Marlene CW Stewart PhD Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UKDr Ioanna Tzoulaki PhD Department pf Epidemiology and Biostatistics, Imperial College London, London, UKDr Sarah G Buxbaum PhD Jackson State University, Jackson, MS, USADr Daphne L van der A PhD National Institute for Public Health and the Environment, Bilthoven, the Netherlands

Dr Nita G Forouhi MRCP, PhD, FFPHMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK

Dr N Charlotte Onland-Moret PhD Julius Center for Health Sciences and Primary Care, UMC UtrechtProfessor Yvonne T van der Schouw PhD Julius Center for Health Sciences and Primary Care, UMC UtrechtDr Renate B Schnabel MD, MSc University Heart Center Hamburg, Department of General and Interventional Cardiology, Hamburg, GermanyDr Jaroslav A Hubacek PhD Centre for Experimental Medicine, Institute of Clinical and Experimental Medicine, Prague, Czech RepublicDr Ruzena Kubinova MD National Institute of Public Health, Prague, Czech RepublicDr Migle Baceviciene MD, PhD Lithuanian University of Health Sciences, Kaunas, LithuaniaProfessor Abdonas Tamosiunas MD, PhD Lithuanian University of Health Sciences, Institute of Cardiology, Kaunas, Lithuania

Prof Andrzej Pajak MD, PhDDepartment of Epidemiology and Population Studies, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

Dr Roman Topor-Madry MD, PhDDepartment of Epidemiology and Population Studies, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

Dr Urszula Stepaniak PhDDepartment of Epidemiology and Population Studies, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

Professor Sofia Malyutina MD, PhDInstitute of Internal and Preventive Medicine, Siberian Branch of Russian Academy of Medical Sciences, Novosibirsk, Russia

Dr Damiano Baldassarre PhDDipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy and Centro Cardiologico Monzino IRCCS Milan, Italy

Dr Bengt Sennblad PhDAtherosclerosis Research Unit, Department of Medicine Solna, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden

Professor Elena Tremoli PhDDipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy and Centro Cardiologico Monzino IRCCS Milan, Italy

Professor Ulf de Faire MD PhD Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, SwedenDr Fabrizio Veglia PhD Biostatistics Unit, Centro Cardiologio Cardiologico Monzino IRCCS, Milano, ItalyProfessor Ian Ford PhD Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UKProfessor J Wouter Jukema MD Dept of Cardiology, Leiden University Medical Center, Leiden, the NetherlandsProfessor Rudi GJ Westendorp MD, PhD Department of Gerontology and Geriatrics , Leiden University Medical Center, The NetherlandsDr Gert Jan de Borst MD, PhD Department of Vascular Surgery, University Medical Center, Utrecht, NetherlandsDr Pim A de Jong MD, PhD University Medical Center Utrecht, Department of RadiologyProfessor Ale Algra MD Department of Neurology and Neurosurgery and Julius Center for Health Sciences and Primary Care, UMC UtrechtDr Wilko Spiering PhD, MD University Medical Center Utrecht, Department of Vascular Medicine, Utrecht, The Netherlands

Dr Anke H Maitland-van der Zee PharmD PhDDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands

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Dr Olaf H Klungel PharmD PhDDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands

Professor Anthonius de Boer MD, PhDDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands

Professor Pieter A Doevendans MD, PhD Dept of Cardiology; Division of heart and lungs; Universtity Medical Center Utrecht, The NetherlandsProfessor Charles B Eaton MD, MS Memorial Hospital of Rhode Island, Rhode Island, USAProfessor Jennifer G Robinson MD, MPH University of Iowa, Iowa, USADr David Duggan PhD Translational Genomics Research Institute, Phoenix, AZ 85004, USA

DIAGRAM ConsortiumMAGIC ConsortiumInterAct Consortium

Professor John Kjekshus MD, PhD Department of Cardiology, Oslo University Hospital Rikshospitalet, University of Oslo, Oslo, Norway

Dr John R Downs MDDepartment of Medicine, University of Texas Health Science Centre, San Antonio TX, and VERDICT, South Texas Veterans Health Care System, San Antonio, TX, USA

Dr Antonio M Gotto MD, DPhil Weill Cornell Medical College, 1305 York Ave Y-805, New York, NY, USADr Anthony C Keech MD, PhD NHMRC Clinical Trials Centre, University of Sydney, Sydney, AustraliaDr Roberto Marchioli MD Hematology/Oncology TDU, Quintiles, Milan, ItalyDr Gianni Tognoni MD Department of Clinical Pharmacology and Epidemiology, Consorzio Mario NegriSud, Santa Maria Imbaro, Chieti, Italy

Professor Peter S Sever PhD, FRCPInternational Centre for Circulatory Health, Imperial College London, 59,61 North Wharf RdLondon W2 1LA

Professor Neil R Poulter MBBS, FRCP, FMedSci International Centre for Circulatory Health, Imperial College London, London, UKDr David D Waters MD Department of Medicine, University of California, San Francisco, California, USADr Terje R Pedersen MD, PhD University of Oslo and Centre for Preventative Medicine, Oslo University Hospital, Ulleval, Oslo, NorwayDr Pierre Amarenco MD Denis Diderot University, Paris, FranceDr Haruo Nakamura MD Mitsukoshi Health and Welfare Foundation, Tokyo, JapanProfessor John JV McMurray MD BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UKDr James D Lewsey PhD Institute of Health and Wellbeing, University of Glasgow, UKDr Daniel I Chasman PhD Division of Preventive Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USADr Paul M Ridker MD Division of Preventive Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USAProf Aldo P Maggioni MD ANMCO Research Center, Via La Marmora 34, 50121 Florence, ItalyProfessor Luigi Tavazzi MD Maria Cecilia Hospital, GVM Care&Research- E.S. Health Science Foundation, Via Corriera, 1 48010 Cotignola (RA), ItalyProfessor Kausik K Ray MD, MPhil Cardiovascular Sciences Research Centre, St George’s University of London

DrSreenivasa Rao Kondapally Seshasai MD, MRCP, PhD Cardiovascular and Cell Sciences Research Institute, St George's, University of London

Professor JoAnn E Manson MD, DrPH Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USADr Jackie F Price MD, FFPH Centre for Population Health Sciences, University of EdinburghProfessor Peter H Whincup MD, PhD St George's University of London, London, UKProfessor Richard W Morris PhD UCL Department of Primary Care & Population Health

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Professor Debbie A Lawlor PhDMRC Integrative Epidemiology Unit at the University of Bristol (IEU) and School of Social and Community Medicine, University of Bristol, Bristol, UK

Professor George Davey Smith MD, PhDMRC Integrative Epidemiology Unit at the University of Bristol (IEU) and School of Social and Community Medicine, University of Bristol, Bristol, UK

Professor Yoav Ben-Shlomo MB BS, PhD School of Social and Community Medicine, University of Bristol, Bristol, UKProfessor Pamela J Schreiner PhD School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America

Professor Myriam Fornage PhDInstitute of Molecular Medicine and Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA

Professor David S Siskovick MD, MPHCardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA

Dr Mary Cushman MD, MSc Departments of Medicine and Pathology, University of Vermont, 208 South Park Dr, Colchester, VT 05446 USADr Meena Kumari PhD Research Department of Epidemiology & Public Health, UCL, London, UK

Professor Nick J Wareham MBBS, PhDMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK

Dr W M Monique Verschuren PhDNational Institute for Public Health and the Environment, Bilthoven, the Netherlands, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

Professor Susan Redline MD, MPH Harvard Medical School, Brigham and Women's Hospital, 221 Longwood Ave Boston MA 02115Dr Sanjay R Patel MD, MS Division of Sleep Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USAProfessor John C Whittaker PhD GlaxoSmithKlineProfessor Anders Hamsten MD PhD FRCP Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, SwedenDr Joseph A Delaney PhD Department of Epidemiology, University of Washington, USA

Dr Caroline Dale BA, MSc, PhDDepartment of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK

Dr Tom R Gaunt PhD MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UKDr Andrew Wong PhD MRC Unit for Lifelong Health & Ageing, Institute of Epidemiology and Health Care, University College London, UKProfessor Diana Kuh PhD, FFPH MRC Unit for Lifelong Health & Ageing, Institute of Epidemiology and Health Care, University College London, UKProfessor Rebecca Hardy PhD MRC Unit for Lifelong Health & Ageing, Institute of Epidemiology and Health Care, University College London, UK

Dr Sekar Kathiresan MD

Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA;Program in Medical and Population Genetics, Broad Institute, Cambridge, MA; Cardiology Division, Massachusetts General Hospital, Boston, MA

Dr Berta A Castillo PhDCenter for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

Professor Pim van der Harst MD, PhD Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The NetherlandsProf Eric J Brunner PhD, FFPH Research Department of Epidemiology & Public Health, UCL, London, UK

Professor Anne Tybjaerg-Hansen MD, DMScDepartment of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark

Professor Sir Michael G Marmot FRCP Research Department of Epidemiology & Public Health, UCL, London, UKDr Ronald M Krauss MD Children's Hospital Oakland Research Institute, Oakland, CA USAProfessor Michael Tsai MD, PhD University of Minnesota, Minneapolis, MS, USAProfessor Josef Coresh MD, PhD, MHS Department of Epidemiology, Johns Hopkins Bloomberg School of Public

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Health, Baltimore, MD, USA

Dr Ronald C Hoogeveen PhDBaylor College of Medicine, Department of Medicine, Division of Atherosclerosis & Vascular Medicine, Houston, Texas 77030

Professor Bruce M Psaty MD, PhD, MPHCardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA,USA

Dr Leslie A Lange PhDDepartment of Genetics, University of North Carolina School of Medicine at Chapel Hill, Chapel Hill, North Carolina 27514, USA

Professor Hakon Hakonarson MD, PhDCenter for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

Dr Frank Dudbridge PhDDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK

Professor Steve E HumphriesPhD MRCpath MRCP FAMS

Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Professor Philippa J Talmud PhD, DScCentre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK

Professor Mika Kivimaki PhD Research Department of Epidemiology & Public Health, UCL, London, UK

Dr Nicholas J Timpson PhDMRC Integrative Epidemiology Unit at the University of Bristol (IEU) and School of Social and Community Medicine, University of Bristol, Bristol, UK

Dr Claudia Langenberg MD PhDMRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK

Professor Folkert W Asselbergs MD PhD

Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht, The Netherlands; Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands; Institute of Cardiovascular Science, faculty of Population Health Sciences, University College London, London, United Kingdom

Professor Mikhail Voevoda MD, PhD

Institute of Internal and Preventive Medicine, Siberian Branch of Russian Academy of Medical Sciences, Novosibirsk, Russia. Institute of Internal Medicine with Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Science

Professor Martin Bobak MD, PhD Research Department of Epidemiology & Public Health, UCL, London, UKDr Hynek Pikhart PhD Research Department of Epidemiology & Public Health, UCL, London, UKProfessor James G Wilson MD Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216 USAProfessor Alex P Reiner MD, MS Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA

Dr Brendan J Keating PhDCenter for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

Professor Aroon D Hingorani** FRCP Institute of Cardiovascular Science, UCL, London, UKProfessor Naveed Sattar** FRCP BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK

* Joint first authors

** Joint senior authors

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Abstract

Background: Statins increase risk of new-onset type 2 diabetes mellitus (T2D) but it is uncertain if this

is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR).

Methods: We used single nucleotide polymorphisms in the HMGCR gene - rs17238484 (for the main

analysis) and rs12916 (for a subsidiary analysis) - as proxies for HMGCR inhibition by statins. In up to

223,463 individuals (43 studies), we examined associations of these variants with plasma lipid, glucose

and insulin concentrations, weight, and prevalent and incident T2D. Study-specific effect estimates

per copy of each LDL-lowering allele were pooled by meta-analysis. The findings were compared with

a meta-analysis of new-onset T2D and weight change data from up to 20 major randomised statin

cardiovascular end-point trials (n=129,170).

Results: Each additional rs17238484-G allele was associated with 0.06mmol/L lower LDL-cholesterol

(95% confidence interval [CI] 0.05-0.07), and higher weight (0.30kg, 95% CI 0.18-0.43), waist

circumference (0.32cm, 95% CI 0.16-0.47), plasma insulin concentration (1.62%, 95% CI 0.53-2.72),

and plasma glucose (0.23%, 95% CI 0.02-0.44). The rs12916 SNP had similar effects on LDL-

cholesterol, weight, and waist circumference. The rs17238484 G-allele that was associated with lower

LDL-cholesterol was associated with higher T2D risk (odds ratio [OR] per allele 1.02, 95% CI 1.00-1.05);

the rs12916 allele association was consistent (OR 1.06, 95% CI 1.03-1.09). Over 4.2 (range 1.9–6.7)

years, statins lowered LDL-cholesterol by 0.92mmol/L (95% CI 0.18-1.67), increased body weight by

0.24kg (95% CI 0.10-0.38) (0.33kg, 95% CI 0.24-0.42, in placebo/standard care-controlled trials; -

0.15kg, 95% CI -0.39-0.08 in intensive vs moderate dose trials) and increased odds of new-onset T2D

(OR 1.12, 95% CI 1.06-1.18) (OR 1.11, 95% CI 1.03 to 1.20 in placebo/standard care-controlled trials;

OR 1.12, 95% CI 1.04 to 1.22 in intensive vs moderate dose trials).

Conclusions: The increased T2D risk observed on statins is at least partially explained by an on-target

effect of HMGCR inhibition.

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Background

Statins reduce low-density lipoprotein cholesterol (LDL-C) concentration by inhibiting 3-hydroxy-3-

methylglutaryl-CoA (HMG-CoA) reductase (HMGCR), leading to a proportionate reduction in

cardiovascular disease (CVD) risk1–4. Consequently, statins have become the most widely prescribed

drug class, with over 25% of US adults aged ≥45 years (30 million individuals) receiving these drugs

from 2005 to 20085, and an estimated 56 million may be eligible for statin therapy under new

guidelines6.

A meta-analysis of randomised controlled trials (RCTs) of statins recently identified higher risk of type

2 diabetes mellitus (T2D) from statin treatment,7 which was later found to be dose-related8. The

findings prompted a US Food and Drug Administration Drug Safety Communication in 20129 and a

change to statin safety labelling. Subsequently, observational studies have also reported associations

of statin treatment with higher T2D risk 10–12. Although T2D is a cardiovascular risk factor, there

remains an unequivocal net benefit of statin treatment for CVD prevention even among patients with

diabetes 13.

However, the mechanism underlying the glucose-elevating effect of statins is of interest. The adoption

of a less healthy lifestyle among statin users is a potential explanation in observational studies, but is

unlikely in blinded treatment trials, suggesting the effect is pharmacological. However, whether it is

explained by the same mechanisms as for LDL-C lowering (i.e. HMG-CoA reductase inhibition) or by

one of the proposed ‘pleiotropic’ effects of statins14 15, e.g. mediated through isoprenoid

intermediates and G-protein signalling16, is uncertain.

To investigate this, we employed the Mendelian randomisation principle17, using common variants in

the gene encoding a drug target as unconfounded, unbiased proxies for pharmacological action on

that target18. We identified single nucleotide polymorphisms (SNPs) in the HMGCR gene and examined

their associations with body weight, body mass index (BMI), waist circumference, plasma insulin and

glucose, and T2D risk. Associations with these phenotypes would implicate a mechanism involving

HMG-CoA reductase inhibition. To test the correspondence of genetic and pharmacological effects,

we updated the meta-analysis of the effect of statins on T2D risk in randomised trials, and added new

information on body weight.

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MethodsGenetic studies

- Instrument selection

We selected as instruments two SNPs (rs17238484 and rs12916) in the HMGCR gene based on

associations with LDL-C in the Whitehall II Study (n=4,678) 19 using the IBC HumanCVD BeadChip

(‘Cardiochip’; Illumina)20 (Supplementary Methods). Both were subsequently associated with LDL-C

at a genome-wide level of statistical significance21, with strong associations in the largest genome-

wide study of lipids to-date22 (rs17238484 p=1.35x10-21; rs12916 p<1.00x10-30). Data were available

for the greatest number of individuals for the rs17238484 SNP, and this was used for the principal

analysis; a subsidiary analysis used the rs12916 SNP. To investigate potential confounding by linkage

disequilibrium (LD) between our lead SNPs and others in nearby genes, we evaluated the association

of the HMGCR SNPs with hepatic genome-wide expression data (Supplementary Methods). If the

lead SNPs were in strong LD with nearby loci, those genes may confound the observed effects of

HMGCR genotype on measured phenotypes 23.

In 43 observational population studies (Supplementary Table 3) with genotype data for the

rs17238484 SNP (or a proxy in strong LD, r2>0.85) we included individuals of European descent for

whom data were available on one or more phenotype of interest. In a secondary analysis, we included

data from a subset of studies with data available on the rs12916 SNP (or a suitable proxy).

- Association with cardiometabolic phenotypes

Biomarkers included in the genetic analysis allowing closest comparison with statin trial data were

total, LDL-, and non-HDL-cholesterol, body weight, body mass index (BMI), waist and hip

circumferences, waist:hip ratio, height, plasma glucose and plasma insulin (Supplementary Methods,

Supplementary Table 4). The primary disease outcome was T2D, including prevalent (occurring prior

to study baseline) as well as incident cases (occurring subsequently) (Supplementary Methods,

Supplementary Table 5). In the Mendelian randomisation paradigm the ‘intervention’ is the naturally

randomised allocation of genotype, which occurs at conception and exerts its effect from that point

throughout the lifetime of the individual. Thus, events prevalent at the time of recruitment to genetic

studies are nevertheless incident from the perspective of the time of the genotypic randomisation

and can be included in the genetic analysis. Thus for the genetic analysis, both prevalent and incident

cases were included to maximise power.

- Statistical analysis

We evaluated study-specific associations of rs17238484 and rs12916 with each continuous trait using

univariate linear regression models. Plasma glucose and insulin were analysed on the natural

logarithmic scale given their skewed distributions, and proportional differences in geometric means

per minor allele are presented. The rs17238484-G allele and rs12916-T allele were each associated

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with lower LDL-C concentration and were designated the effect alleles, to facilitate direct comparison

with statin therapy.

Associations of the rs17238484 and rs12916 SNPs with T2D risk were evaluated using univariate

logistic regression models to estimate the odds ratio OR per LDL-lowering allele. Within-study

estimates were combined using fixed- and random-effects meta-analysis, with heterogeneity

quantified by the I2 statistic24. Heterogeneity between subgroups was evaluated using meta-

regression. All genetic analyses were performed using a pre-specified routine in Stata v12.1 (Stata

Corp, College Station, Texas), which was translated for use in SPSS, SAS and R where necessary.

In order to corroborate our findings, we examined the associations of the two lead SNPs in a large

GWA study of BMI25, a Metabochip analysis of plasma insulin26, and a GWA and Metabochip analysis

of T2D27.

Meta-analysis of statin trials

- Identification of trials

We updated our two summary level meta-analyses on the association of statin treatment with

incident T2D in cardiovascular prevention trials of ≥1000 participants, followed for ≥1 year 7,8. For

details of the exclusion criteria and trials see Supplementary Methods.

- Outcomes

Investigators from 20 eligible trials with data on incident T2D were contacted for information on

weight change during follow-up by treatment allocation, which was used as a co-primary outcome. 15

trials provided data on weight at baseline and at the last visit attended in individuals free from T2D at

baseline (Table 1). Two trials (ALLHAT28 and A to Z29) did not measure weight sequentially, and weight

data were unavailable from the remaining three trials. Data were also analysed separately for

participants not experiencing any primary cardiovascular outcome (according to trial-specific

definitions) to exclude the possibility that the effect of statin therapy on weight was limited to

participants experiencing cardiovascular events.

Changes in LDL-C in each treatment arm at 1 year were available from the Cholesterol Treatment

Trialists’ Collaboration meta-analysis for 18 trials1 while data for mean changes in LDL-C during two

trials were taken from the primary publications30,31. Information about plasma glucose and insulin

concentrations, BMI, waist circumference and waist:hip ratio was unavailable.

- Statistical analysis

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Within-trial ORs for T2D during follow-up in participants free from T2D at baseline and within-trial

mean differences in weight change between treatment arms (calculated as the difference from

baseline to final visit) were synthesised using random- and fixed-effects meta-analyses. We undertook

meta-regression analysis of the associations of new-onset T2D and weight change with change in LDL-

C at one year and with follow-up duration. Inter-study heterogeneity was assessed using the I2

statistic. Stata version 10.1 (Stata Corp., College Station, Texas) was used for trial-related analyses.

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Findings

Selection and specificity of the genetic instruments

Of 38 Cardiochip SNPs within 55kb of the HMGCR gene, seven met pre-specified criteria for

instrument selection (Supplementary Methods), of which all but the two selected, rs17238484 and

rs12916, were in strong LD (r2>0.9) (Supplementary Table 1). Gene expression data for rs17238484

were unavailable, but the T allele of rs12916 is associated with lower hepatic HMGCR expression

(p=1.30x10-5) but not with expression of adjacent genes (Supplementary Results and Supplementary

Figures 1 and 2).

Studies contributing to the genetic analysis

Data for up to 195,444 individuals (43 studies) for the HMGCR rs17238484 SNP and 94,652 individuals

(21 studies) for the rs12916 SNP (or suitable proxies in studies where these were not directly

measured) contributed to the analysis of genetic associations with biomarkers and outcomes. The

mean age of study participants was 59 years (range 26-75) (Supplementary Table 3).

Genetic association with lipid biomarkers

The association of rs17238484 genotype with circulating concentrations of major lipid fractions

followed an additive model in the meta-analysis of available data (Figure 1a). Each additional

rs17238484-G allele was associated with 0.06mmol/L (95% CI 0.05-0.07) lower LDL-C (p=1.34x10 -35;

101,919 individuals, 26 studies), 0.07mmol/L (95% CI 0.06-0.08) lower total cholesterol (p=6.46x10 -36;

117,545 individuals, 30 studies), and 0.07mmol/L (95% CI 0.06-0.08) lower non-HDL-C (p=3.32x10 -30

103,375, 27 studies). The association of genotype with LDL-C concentration was consistent between

subgroups (data available in up to 29 studies, 116,327 individuals), with all meta-regression p-values

>0.05 (Supplementary Table 6). Associations of rs12916 with plasma lipids were directionally

concordant with rs17238484 and of similar magnitude (Supplementary Table 7).

Genetic associations with T2D-related biomarkers

The rs17238484-G allele was associated with 1.62% (95% CI 0.53-2.72; p=0.004) higher plasma insulin

(37,453 individuals, 12 studies), and with higher plasma glucose (0.23% per allele, 95% CI 0.02-0.44;

p=0.03; 73,490 individuals, 23 studies) (Figure 1c). Each rs17238484-G allele was also associated with

0.30kg higher body weight (95% CI 0.18-0.43; p=3.15x10-6; 143,113 individuals, 30 studies), and

0.11kg/m2 higher BMI (95% CI 0.07-0.14; p=1.77x10-7; 152,004 individuals, 32 studies) (Figure 1d), but

not with height (p=0.23; 77,291 individuals, 23 studies). Each additional rs17238484-G allele was

associated with greater waist circumference (0.32cm, 95% CI 0.16-0.47; p=8.32x10-5; 69,163

individuals, 19 studies), hip circumference (0.21 cm, 95% CI 0.10-0.32; p=1.67x10-4; 69,159 individuals,

19 studies), and waist:hip ratio (0.001, 95% CI 0.0003-0.002; p=0.01; 95,496 individuals, 23 studies)

(Figure 1e). The rs12916 SNP showed directionally concordant associations with these biomarkers

(Supplementary Table 7). Additive association patterns were observed with all these traits, and no

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differences in the rs17238484 SNP effect were noted between subgroups (all meta-regression p-

values >0.05) (Supplementary Table 6). Estimates from random-effects meta-analysis are shown in

Supplementary Table 8.

Interrogation of data from a meta-analysis of genome-wide association (GWA) studies of BMI25and an

Illumina Metabochip-based32 analysis of plasma insulin26 revealed directionally concordant

associations of the rs17238484 and rs12916 SNPs (or suitable proxies) with both these traits: log

plasma insulin rs12916 β=0.007, 95% 0.002 to 0.012, p=4.72x10-3; rs17238484 β=0.01, 95% 0.004 to

0.016, p=5.92x10-4, and BMI rs17238484 p=9.28x10-6; rs12916 p=1.45x10-4. Associations of both SNPs

with fasting insulin were attenuated to the null after adjustment for BMI in the same datasets

(rs17238484 p=0.74; rs12916 p=0.63).

Genetic association with T2D risk

In 26,236 cases and 164,021 controls in our 35 population studies, the HMGCR rs17238484-G allele

(associated with lower LDL-C and higher body weight and BMI) was associated with higher T2D risk

(OR per allele 1.02, 95% CI 1.00-1.05; p=0.09) (Figures 1b & 2). Data on the HMGCR rs12916 -T2D

association were available for 14,976 cases and 73,574 controls (16 studies). The OR per rs12916-T

allele was 1.06 (95% CI 1.03-1.09, p=9.58x10-5). The associations of both SNPs were confirmed when

our data were combined in a meta-analysis with those from a large GWA and Metabochip study of

T2D risk27 (rs17238484 OR 1.03 95% CI 1.01-1.06; rs12916 OR 1.02 95% CI 1.00-1.04; see

Supplementary Table 10).

Effect of statin therapy on T2D risk

Data were available from 20 statin trials including 129,170 participants free from T2D at baseline

(Table 1). Mean LDL-C reduction across all 20 trials was 0.92 mmol/L (95% CI 0.18-1.67), 1.07 mmol/L

(95% CI 0.44-1.70) in the 15 placebo- and standard care-controlled trials (96,418 individuals), and 0.50

mmol/L (95% CI 0.25-0.76) in the intensive- vs. moderate-dose trials at 1-year follow-up (32,752

individuals).

Mean follow-up across all 20 trials was 4.2 years (range 1.9-6.7). Over this time, 3,858 individuals

allocated to statin or intensive-dose statin and 3,481 allocated to placebo, standard care or moderate-

dose statin were diagnosed with new-onset T2D. The OR for new-onset T2D with statin therapy was

1.12 (95% CI 1.06-1.18) (Figure 3), with little heterogeneity between trial-specific ORs (I2=16%, 95% CI

0-51%) (Supplementary Table 9 provides fixed-effects meta-analysis estimates). There was no

association between LDL-C lowering at 1 year and within-trial ORs for new-onset T2D (log-odds per %

reduction in LDL-C 0.004, 95% CI -0.001-0.009, p=0.10; Supplementary Figure 4), nor between

duration of follow-up and risk of T2D in either univariate meta-regression (log-odds per year increase

in trial duration -0.021, 95% CI -0.058-0.017; p=0.26), or after adjustment for trial type (i.e. placebo-

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and standard care-controlled or intensive vs. moderate statin dose) and per cent LDL-C change (log-

odds -0.006, 95% CI -0.051-0.039; p=0.77).

Effect of statin therapy on body weight

Data were available from 15 trials, including 91,393 participants free from T2D at baseline. Mean

follow-up was 3.9 years (range 1.9-5.9). Recipients of statin therapy or intensive-dose statin therapy

were 0.24kg (95% CI 0.10-0.38kg) heavier by the end of follow-up compared with control recipients in

random effects meta-analysis (Figure 4), although there was substantial heterogeneity between trials

(I2=79%, 95% CI 65-87%). Supplementary Table 9 provides fixed-effects meta-analysis estimates.

When limited to individuals not experiencing a cardiovascular event, estimates were similar (0.21kg,

95% CI 0.08-0.35kg; 83,959 individuals). The effect on weight change was observed only in trials

comparing statin therapy with placebo or standard care (0.33kg, 95% CI 0.24-0.42; I2=18.6%), but not

in trials comparing moderate- with intensive- dose statin therapy (-0.15kg, 95% CI -0.39-0.08;

I2=63.2%). No association was demonstrated between relative LDL-C reduction and within-trial weight

change and (meta-regression β=0.004, 95% CI -0.012-0.021; p=0.58; Supplementary Figure 5). There

was no relationship between duration of follow-up and weight change in either univariate meta-

regression (β=-0.028 kg/year, 95% CI -0.147-0.092; p=0.63) or multivariate meta-regression analysis

(β=-0.009, 95% CI -0.091-0.073; p=0.81) after adjustment for relative LDL-C change and trial type. No

relationship was observed between weight change and risk of new-onset T2D and across the trials

(log-odds per 1kg weight increase -0.14, 95% CI -0.41-0.13; p=0.29).

Role of the funding source

The funding sources had no role in study design, in the collection, analysis, and interpretation of data,

in the writing of the report, or in the decision to submit for publication. The corresponding authors

(DIS and DP) and co-senior authors (ADH and NS) had full access to all data in the study and had final

responsibility for the decision to submit for publication.

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Interpretation

HMGCR genetic variants (in population studies) and statin treatment (in trials) were both associated

with higher T2D risk and higher body weight, indicating these effects are a consequence of HMGCR

inhibition. The association of HMGCR SNPs with T2D risk is novel as is the association of statin

treatment and HMGCR SNPs with higher body weight.

Higher body weight has been shown to play a causal role in the development of T2D33, suggesting a

possible mechanism for the dysglycaemic effect of statin treatment. However, whether the

relationship between HMGCR inhibition and T2D is mediated exclusively by changes in body

composition remains uncertain. Statin treatment led to higher weight and T2D risk, and both HMGCR

SNPs studied were associated with higher body weight and waist circumference, and one with plasma

insulin and glucose concentrations. Insulin resistance may accompany weight gain and a central

distribution of adipose tissue. However, we were unable to identify a specific association of statin

treatment with insulin resistance in these analyses because the relevant measures were unavailable

from trials. One small trial ineligible for the current study reported two months of atorvastatin

treatment led to higher glycosylated haemoglobin (HbA1c) and insulin concentrations and lower

insulin sensitivity, while a previous meta-analysis of statin trials suggested differential effects on

insulin sensitivity between statins34. In JUPITER35 and PROVE-IT TIMI 2236, small increases in HbA1c

were noted in individuals randomised to statin therapy compared with control, while in AFORRD,

HbA1c also increased modestly on atorvastatin compared to placebo after 4 months37. Nevertheless,

the association of one HMGCR SNP with fasting insulin and glucose concentrations, and its

attenuation to the null after adjustment for BMI, support this as a possible mechanistic explanation.

Conversely, the magnitude of weight gain we observed in both statin trials and genetic studies

appears insufficient to account for the corresponding degree of T2D risk. Treatment with more

intensive statin therapy also showed no greater effect on body weight than low/moderate dose

therapy, although T2D risk was greater with intensive statin treatment.

The anatomical site of the genetic and drug effects on energy metabolism that we observe is not

completely certain. The liver is a likely location, given its important involvement in lipid metabolism,

however the dysglycaemic phenotypes seen here may due to modulation of HMGCR function in

skeletal muscle. The possibility also remains that additional off-target effects of statins make a further

contribution to weight gain38.

Inhibition of HMGCR by statins impairs hepatocyte cholesterol synthesis, up-regulates hepatic LDL

receptor expression and reduces circulating LDL-C concentration. Although the genetic findings

provide strong evidence that the effect of statins on weight and T2D risk is due to HMGCR inhibition,

it remains unclear if this effect requires or is independent of reductions in circulating LDL-C. A meta-

regression analysis of trial data did not provide strong evidence for an association between LDL-C

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reduction and body weight or T2D risk, but these analyses were conducted with summary-level data,

which may have limited our ability to detect any such relationship. Studies of genetic variants from

other loci influencing LDL-C39 or drugs lowering LDL-C by other mechanisms may help to resolve this

uncertainty.

An association with BMI has been previously identified for a SNP 350kb from HMGCR in a GWA

study25, though with no other variants within the HMGCR gene. In the present analysis, associations of

the rs17238484 and rs12916 SNPs with BMI and plasma insulin were found in two GWA studies25,26,

though at significance levels above the conventionally stringent thresholds (see Findings for effect

sizes and p-values). This evidence, the consistent effect of both SNPs on LDL-C, and a specific

association with hepatocyte HMGCR mRNA expression for one of the SNPs supports their validity as

genetic instruments in this analysis.

We used two HMGCR SNPs in the genetic analysis, one for the main (rs17238484) and another

(rs12916) for a subsidiary analysis. Although the findings were broadly consistent the small

differences in effect estimates between the two variants could be due to the different allele

frequencies, available sample size for each, and the association of each with unobserved functional

variant or variants.

Certain limitations of the study are noteworthy. Not all phenotypes measured in genetic studies were

available in the statin trials, notably plasma glucose and insulin, waist and hip circumference, and

waist:hip ratio. Moreover, not all studies in the genetic analysis measured glucose in fasting samples.

Given the wide age range of participants included in these analyses it is possible that survival bias may

have influenced our findings; this is, however, is unlikely and any such effect, if present, would likely

be very limited. It is possible that the HMGCR variants may influence the odds of being prescribed

lipid-lowering drugs and thus introduce bias to the HMGCR-T2D risk association. We, however, found

no evidence of an interaction between genotype, lipid-lowering drug use at study baseline and T2D

risk (Supplementary Table 6). The source of the heterogeneity between statin trials contributing body

weight data, particularly for dose-comparison trials, remains uncertain. However, LDL-C reduction

between arms in the dose comparison trials was smaller than that achieved in the placebo-controlled

trials. Our analysis was confined to participants free from T2D at baseline. However, we did not have

access to data on within-trial death, withdrawal or loss to follow up. Although observational

pharmacoepidemiological studies have also examined the association of statin prescription with the

development of T2D, studies of this type can be prone to confounding and bias. For this reason, and

to permit more direct comparison with the genetic analysis, we focused on data from randomised

trials. Finally, trial analyses were performed with summary-level data, which limited power for meta-

regression.

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Our findings pertain to the mechanism by which statins modestly increase the risk of T2D, an

association that has already been established. Recent analyses of trials have shown that though this

association is robust, the absolute risk of developing T2D is greatly offset by the benefits of statin

treatment for CVD risk12,37. Indeed, the efficacy of statin therapy for CVD risk reduction has been

demonstrated conclusively in numerous large primary and secondary prevention RCTs, including

individuals with T2D, with a very favourable risk/benefit profile1,3,4. For this reason, our findings are

mechanistically important but should not alter current guidance on statin prescription for CVD

prevention. Nevertheless, our new results including demonstration of weight increase with statin

therapy suggest lifestyle interventions such as weight optimisation, healthy diet and adequate

physical activity should be more carefully emphasised as important adjuncts to CVD prevention with

statin therapy to attenuate T2D risks. The reason why weight change does not appear to be greater

with intensive statin treatment compared with moderate dose treatment requires further

investigation.

In conclusion, both statin treatment (based on trial results) and carriage of common SNPs in the

HMGCR gene in population studies were associated with weight gain and higher risk of T2D. Weight

gain is physiologically linked to insulin resistance and one of the strongest risk factors for T2D, which

may partly explain the higher risk of T2D in statin-treated patients.

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Funding

DI Swerdlow is supported by a Medical Research Council Doctoral Training Award and a grant from the Rosetrees Trust.MV Holmes is supported by a Medical Research Council Population Health Scientist Fellowship (G0802432).AD Hingorani and JP Casas are supported by University College London NIHR Biomedical Research CentreJEL Engmann is supported by grants from the National Institutes of Health and the Medical Research Council.R Sofat has been supported by a British Heart Foundation (Schillingford) Clinical Training Fellowship (FS/07/011).S Stender is supported by the Danish Medical Research Council (grant no. 10-083788), the Research Fund at Rigshospitalet, Copenhagen University Hospital, and Chief Physician Johan Boserup and Lise Boserup’s Fund.C Chung and A Peasey are supported by grants from the Wellcome Trust (064947/Z/01/Z and 081081/Z/06/Z), a grant from the National Institute on Aging (1R01 AG23522-01), and a grant from MacArthur Foundation.N Forouhi is supported by a grant from the Medical Research Council.A Pajak is supported by grants from the Wellcome Trust (064947/Z/01/Z and 081081/Z/06/Z) and National Institute of Aging (1R01 AG23522-01).R Westendorp is supported by the National Institute for Healthy Ageing (Grant 05060810)J Price is supported by the British Heart Foundation, Chest, Heart and Stroke Scotland, the Wellcome Trust, and the Medical Research Council.R Krauss is supported by the NIH (grant NIH U19 HL065797).SE Humphries and P Talmud are supported by the British Heart Foundation (BHF RG 08/008, PG/07/133/24260), Medical Research Council, the US National Institutes of Health (grant NHLBI 33014) and Du Pont Pharma, Wilmington, USA.M Kivimäki is supported by the National Institute of Aging (AG034454), the Medical Research Council (K013351), and the National Heart, Lung and Blood Institute (HL036310), and the Academy of Finland.DA Lawlor, G Davey Smith, and NJ Timpson work in a Unit that receives funding from the MRC and University of Bristol.N Timpson is supported by the Medical Research Council (grant G0600705).FW Asselbergs is supported by a clinical fellowship from the Netherlands Organisation for Health Research and Development (ZonMw grant 90700342).JA Hubacek is supported by the project (Ministry of Health, Czech Republic) for the development of research organisation 00023001 (IKEM, Prague, Czech Republic) – institutional supportB Sennblad is supported by the Magnus Bergvall Foundation and the Foundation for Old Servants.S Buxbaum was supported by an award from the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health (award number P20MD006899).E Brunner’s research is supported by a British Heart Foundation programme grant (RG/13/2/30098) and the MooDFOOD Collaborative Project (FP7 grant 613598).N Sattar’s research is supported by British Heart Foundation, Diabetes UK, and the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant n° 115372)

The UCLEB (UCL-LSHTM-Edinburgh-Bristol) Consortium (including BRHS, BWHHS, CaPS, EAAS, ELSA, ET2DS, NPHS-II and WHII) was supported by a programme grant from the British Heart Foundation (RG/10/12/28456).

The Utrecht Cardiovascular Pharmacogenetics Study (UCP) was funded by Veni grant Organization for Scientific Research (NWO), Grant no. 2001.064 Netherlands Heart Foundation (NHS), and TI Pharma Grant T6-101 Mondriaan. The Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, has received unrestricted research funding from the Netherlands Organisation for Health Research and Development (ZonMW), the Dutch Health Care Insurance Board (CVZ), the Royal Dutch Pharmacists Association (KNMP), the private-public funded Top Institute Pharma ( www.tipharma.nl, includes co-funding from universities, government, and industry), the EU Innovative Medicines Initiative (IMI), EU 7th Framework Program

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(FP7), the Dutch Medicines Evaluation Board, the Dutch Ministry of Health and industry (including GlaxoSmithKline, Pfizer, and others)."

The Candidate Gene Association Resource (CARe) was supported by contract no. HHSN268200625226C and from the National Institutes of Health/NHLBI, and subcontract no. 5215810-55000000041 to C.L.W. A full listing of the grants and contracts that have supported CARe is provided at http://public.nhlbi.nih.gov/GeneticsGenomics/home/care.aspx. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts N01 HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169 and RR-024156.

The Cardiovascular Health Study (CHS) was supported by contracts HHSN268201200036C, HHSN268200800007C, N01 HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at www.CHS-NHLBI.org.

The EPIC-Netherlands (EPIC-NL) study was funded by ‘Europe against Cancer’ Programme of the European Commission (SANCO); the Dutch Ministry of Public Health, Welfare and Sports (formerly Ministry of Welfare, Public Health and Culture); the Dutch Cancer Society; ZonMW the Netherlands Organisation for Health Research and Development; and World Cancer Research Fund (WCRF). Genotyping of the IBC-chip was funded by IOP Genomics grant IGE05012 from NL Agency.

The Fenland Study is funded by the Wellcome Trust and the Medical Research Council.

The Ely Study was funded by the Medical Research Council, Diabetes UK, and Eastern Region NHS R&D.

The IMPROVE study was supported by the European Commission (Contract number: QLG1- CT- 2002- 00896), the Swedish Heart-Lung Foundation, the Swedish Research Council (projects 8691 and 0593), the Foundation for Strategic Research, the Stockholm County Council (project 562183), the Foundation for Strategic Research, the Academy of Finland (Grant #110413), the Knut and Alice Wallenberg Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Programme of the Karolinska Institutet and the British Heart Foundation (RG2008/014).

The Prospective Study of Pravastatin in the Elderly at Risk (PROSPER) study was supported by a European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° HEALTH-F2-2009-223004 PHASE, grants from the Interuniversity Cardiology Institute of the Netherlands (ICIN) and the Durrer Center for Cardiogenetic Research both Institutes of the Netherlands Royal Academy of Arts and Sciences (KNAW) the Netherlands Consortium for Healthy Ageing (NCHA).

The Women’s Health Initiative (WHI) is funded by the National Heart, Lung and Blood Institute (grant RFP-NHLBI-WH-11-10 - Women’s Health Initiative Extension 2010-2015), US Department of Health and Human Services. Clinical trial registration NCT00000611.

Funding for the AFCAPS/TexCAPS trial was provided by Merck & Co.

The ASCOTT-LLA trial was funded by Pfizer Inc.

GISSI-PREVENTION was funded by Bristol-Myers Squibb, Pharmacia-Upjohn, Societa Prodotti, Antibiotici, Pfizer.

GISSI-HF was funded by SPA, Pfizer, Sigma Tau, and AstraZeneca

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DNA extraction in the Caerphilly Prospective Study (CaPS) study was funded by a grant from the Medical Research Council.

The British Women's Heart and Health Study (BWHHS) is supported by funding from the British Heart Foundation (BHF) and the Department of Health Policy Research Programme (England). HumanCVD genotyping of the BWHHHS was funded by the BHF [PG/07/131/24254].

The British Regional Heart Study (BRHS) is a BHF research group and is supported by a BHF programme grant RG/08/013/25942.

The MRC National Survey of Health and Development (MRC NSHD) is supported by the Medical Research Council (MC_UU_12019/1).

The Prevention of Renal and Vascular Endstage Disease Study (PREVEND) genetics is supported by the Dutch Kidney Foundation (Grant E033), the EU project grant GENECURE (FP-6 LSHM CT 2006 037697), the National Institutes of Health (grant LM010098), the Netherlands organisation for health research and development (NWO VENI grant 916.761.70), and the Dutch Inter University Cardiology Institute Netherlands (ICIN).

The Copenhagen City Heart Study (CCHS) is supported by the Danish Medical Research Council (grant no. 10-083788), the Research Fund at Rigshospitalet, Copenhagen University Hospital, Chief Physician Johan Boserup and Lise Boserup’s Fund, Ingeborg and Leo Dannin’s Grant, Henry Hansen and Wife’s grant, and a grant from the Odd Fellow Order.

The Whitehall II Study (WHII) is supported by the National Institute on Aging, the Medical Research Council (K013351), the British Heart Foundation and the National Heart, Lung and Blood Institute (HL036310).

The Atherosclerosis Risk in Communities Study (ARIC) is carried out as a collaborativestudy supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402;and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research.

The Health, Alcohol and Psychosocial factors In Eastern Europe studies (HAPIEE) are supported by Wellcome Trust (064947/Z/01/Z and 081081/Z/06/Z); a grant from the National Institute on Aging (1R01 AG23522-01); and a grant from MacArthur Foundation.

The Edinburgh Artery Study (EAS) was funded by the British Heart Foundation.

The Aspirin in Asymptomatic Atherosclerosis Trial (AAA) was funded by the British Heart Foundation and the Chief Scientist Office, Scotland.

The Edinburgh Type 2 Diabetes Study (ET2DS) was funded by the Medical Research Council of the United Kingdom (project grant G0500877).

The Second Manifestations of Arterial Disease study (SMART) was financially supported by the Biobanking and Biomolecular Research Infrastructure BBMRI-NL, financed by the Dutch government (NWO 184.021.007).

The Women's Genome Health Study (WGHS) is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute, with collaborative scientific support and funding for genotyping provided by Amgen.

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The Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin trial (JUPITER) and genetic analysis in the JUPITER population were funded by AstraZeneca.

The Coronary Artery Risk Development in Young Adults (CARDIA) Study is conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201300025C & HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. This manuscript has been reviewed and approved by CARDIA for scientific content.

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Ethics committee approval

All studies contributing data to these analyses were approved by their local ethics committees, as described in the published findings of each study (citations included below and in the Supplementary Material).

Conflicts of interest

P Amarenco has received funds from Pfizer for board membership, consultancy, grants, speaking engagements and the development of educational presentations.

A Gotto has received funds for board membership of Aegerion, Arisaph, DuPont, VascuVis, and Vatera, consultancy for Janssen, Kowa, Merck and Roche, and for manuscript preparation for AstraZeneca.

R Hoogeveen has received funds for board membership of Liposcience Inc., for speaking engagements for Denka Seiken Co., and in the form of grants to his institution from Merck/Schering-Plough, Diadexus Inc. and Denka Seiken Co..

A Keech has received funds in the form of grants to his institution, consultancy fees and travel support from Bristol-Myers Squibb, consultancy fees from Astra-Zeneca, Merck, Novartis and Pfizer, grants paid to his institution from Astra-Zeneca, Merck, Novartis and Pfizer, and for speaking engagements from Astra-Zeneca, Merck, Novartis and Pfizer.

R Krauss has received funds for advisory board membership from Merck, consultancy fees from Celera and Genentech, grants from Quest Diagnostics, and his institution receives funds resulting from a patent related to diagnostic use of a HMGCR spliced isoform.

JE Manson is named as a coinventor on a patent held by the Brigham and Women’s Hospital that relates to inflammatory biomarkers in diabetes prediction.

R Marchioli has received funds for speaking engagements from Ferrer, Pronova BioPharma, Sigma-Tau and Societa Prodotti Antibiotica, and his institution has received funds from Sigma-Tau, Societa Prodotti Antibiotica, Glaxo-SmithKline, Novartis, AMGEN, Pronova BioPharma and General Electric. J McMurray has received reimbursement for travel from AstraZeneca, and his institution has received funds from AstraZeneca.

T Pedersen has received consultancy fees, grants and fees for speaking engagements from Merck, and fees for speaking engagements from AstraZeneca, Roche and AMGEN.

N Poulter has received fees for speaking engagements from several pharmaceuticals manufacturers, fees for production of books from Servier, and his institution has received grants from Pfizer and the Hypertension Trust.

K Ray has received fees for advisory board membership from Pfizer, for involvement in trial management and advisory boards for Roche, for speaking engagements and advisory board membership from MSD, for speaking engagements, advisory board membership and trial involvement from AstraZeneca, for advisory board membership and trial involvement from Sanofi, for advisory board membership from Aegerion, Regeneron and Abbott, for speaking engagements from Menarini, Novo Nordisk and theHeart.org, for trial involvement and steering committee membership from GSK, and for advisory board membership from Novartis.

J Robinson’s institution has received grants for her work from AMGEN, Daiichi Sankyo, Esperion, GSK, Merck, Genentech/Hoffman-La Roche and Zinfandel/Takeda.

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P Sever has received consultancy fees from Pfizer and Servier, fees for speaking engagements from Pfizer and Servier, and fees for development of educational presentations from Pfizer, and his institution has received funds for his work from Pfizer and Servier.

L Tavazzi’s institution received funds for his work from the ANMCO Foundation.

D Waters has received consultancy fees from Merck-Schering Plough and Pfizer, and fees for speaking engagements from Pfizer.

J Whittaker is an employee of and holds stock in GlaxoSmithKline.

JW Jukema has received research grants from and was speaker on (CME accredited) meetings sponsored by Astellas, Anthera, Astra-Zeneca, Bayer, Biotronik, Boston Scientific,Correvio, Daiichi Sankyo, Lilly, Genzyme, Medtronic, Merck-Schering-Plough, Pfizer, Orbus Neich, Novartis, Roche, Servier, Sanofi Aventis, the Netherlands Heart Foundation, the Interuniversity Cardiology Institute of the Netherlands and the European Community Framework KP7 Programme.

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Table1. Statin trial characteristics

Figures:1 a. SNP-lipids associations; b. SNP-T2D risk associations; c.SNP-glucose and insulin associations; d.

SNP-BMI, weight and height associations; e. SNP – waist, hip and waist:hip2 SNP-T2D meta-analysis risk forest plot3 Statins-T2D risk meta-analysis forest plot4 Statins-weight change meta-analysis forest plot

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Table 1. Baseline data for participants without diabetes in twenty large statin trials listed in chronological order of publication

Trial N (statin / control)

Treatment (active / control)

Follow up

(years)

Trial population Age (years

)

Diabetes diagnostic

criteria#

Weight change data

available

Absolute (%) LDL-c lowering

at 1 year†

No. of cases of T2D on statin (or intensive

statin)

No. of cases of T2D on

control (or low dose

statin)4S 4,242

(2,116 / 2,126)S10-40mg / placebo

5.2 Angina or previous MI 59 i, ii, iii Yes -1.77 (-37%) 198 193

WOSCOPS 5,974(2,999 / 2,975)

P40mg / placebo

4.8 Male, hypercholesterolemia, no history of MI

55 ii, iii Yes -1.07 (-24%) 75 93

AFCAPS TexCAPS 6,211(3,094 / 3,117)

L20-40mg / placebo

5.2 Average cholesterol levels, no CVD

58 i, ii, iii Yes -0.94 (-27%) 72 74

LIPID 6,997(3,496 / 3,501)

P40mg / placebo

5.9* hospitalisation for unstable angina or previous MI

62* ii, iii Yes -1.03 (-25%) 126 138

GISSI-Prevenzione

3,460(1,743 / 1,717)

P20mg / standard care

1.9 Recent MI 59 iii Yes -0.35 (-12%) 96 105

LIPS 1,475(724 / 751)

F80mg / placebo

3.9* Recent percutaneous coronary intervention

60 i No -0.92 (-27%) 17 14

HPS 14,573(7,291 / 7,282)

S40mg / placebo

5.0 CVD or diabetes 65 i, ii No -1.29 (-29%) 335 293

PROSPER 5,023(2,510 / 2,513)

P40mg / placebo

3.2 Age 70-82 years with CVD or risk factors

75 ii, iii Yes -1.04 (-31%) 165 127

ALLHAT-LLT 6,087(3,017 / 3,070)

P40mg / no treatment

4.8 CHD or CHD risk factors 66 iii No -0.54 (-18%) 238 212

ASCOT-LLA 7,773(3,910 / 3,863)

A10mg / placebo

3.3* Hypertension, no CHD 63 iv Yes -1.07 (-35%) 154 134

PROVE-IT TIMI 22

3,395(1,707 / 1,688)

A80mg/ P40mg

2.0 Recent hospitalisation for ACS

58 i, ii, iii Yes -0.65 (-22%) 101 99

A to Z 3,504(1,768 / 1,736)

S40-80mg/ Placebo – S20mg

2.0* Recent hospitalisation for ACS

60 i, ii No -0.30 (-15%) 65 47

TNT 7,595(3,798 / 3,797)

A80mg / A10mg

5.0 Stable CHD 61 i, ii, iii Yes -0.62 (-22%) 418 358

IDEAL 7,461 A80mg / S20- 4.8* Previous MI 62 i, ii, iii Yes -0.55 (-16%) 240 209

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(3,737 / 3,724) 40mgSPARCL 3,803

(1,905 / 1,898)A80mg / placebo

4.4 Recent stroke or transient ischemic attack

63 i, ii, iii‡ Yes -1.43 (-42%) 166 115

MEGA 6,086(3,013 / 3,073)

P10-20mg / no treatment

5.3 Hypercholesterolemia, no previous CHD or stroke

58 i, ii, iii Yes -0.67 (-17%) 172 164

CORONA 3,534(1,771 / 1,763)

R10mg / placebo

2.5 Systolic heart failure 73 i Yes -1.63 (-45%) 100 88

JUPITER 17,802(8,901 / 8,901)

R20mg / placebo

1.9* No CVD, no diabetes, hsCRP ≥2.0mg/L

66* i, ii Yes -1.09 (-50%) 270 216

GISSI-HF 3,378(1,660 / 1,718)

R10mg / placebo

3.6 Chronic heart failure 67 iii Yes -0.92 (-35%) 225 215

SEARCH 10,797(5,398 / 5,399)

S80mg / S20mg

6.7 Previous MI 64 i No -0.39 (-12%) 625 587

TOTAL 127,695(63,834/63,861)

- 4.2 (1.6)

- - - 3,858 3,481

*median values ‡ included criterion that diagnostic elevated fasting plasma glucose must be ≥2.0mmol/L higher than baseline glucose† change in lipid values at 1 year except for SPARCL (average difference during trial) and CORONA (difference at 3 months)# Diagnostic criteria - i: adverse event report and/or physician report; ii: glucose lowering therapy; iii: elevated fasting plasma glucose (≥7.0mmol/L) on at least one occasion;

Abbreviations: Scandinavian Simvastatin Survival Study (4S), West of Scotland Coronary Prevention Study (WOSCOPS), Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS TexCAPS), Long-term Intervention with Pravastatin in Ischaemic Disease (LIPID), Gruppo Italiano per lo Studio della Sopravvivenza nell'Insufficienza cardiaca (GISSI) Prevenzione, Heart Protection Study (HPS), Prospective Study of Pravastatin in the Elderly at Risk (PROSPER) trial, Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT), Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm (ASCOT-LLA), Pravastatin or Atorvastatin Evaluation and Infection Therapy (PROVE-IT TIMI 22) study, Aggrastat to Zocor (A to Z) study, Treating to New Targets (TNT) study, Incremental Decrease in Events through Aggressive Lipid Lowering (IDEAL) study, Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial, Management of Elevated Cholesterol in the Primary Prevention Group of Adult Japanese Study Group (MEGA), Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA), JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin), GISSI-Heart Failure, SEARCH (Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine); S: simvastatin; P: pravastatin; L: lovastatin; A: atorvastatin; F: fluvastatin; R: rosuvastatin; MI: myocardial infarction; CVD: cardiovascular disease; CHD: coronary heart disease

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Figure 1

a - Associations of HMGCR rs17238484 with major plasma lipid fractions

b - Association of HMGCR rs17238484 with risk of T2D

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567

89

c - Associations of HMGCR rs17238484 with plasma glucose and plasma insulin

d - Associations of HMGCR rs17238484 with BMI and body weight

30

1

234

5678

e - Associations of HMGCR rs17238484 with waist circumference, hip circumference and waist:hip ratio

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Figure 2 - Association of HMGCR rs17238484 and rs12916 with risk of type 2 diabetes (fixed effects meta-analysis)

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Figure 3 - Effect of statin therapy on new-onset type 2 diabetes in 20 trials (random effects meta-analysis)

Higher T2D odds in treatment arm

Lower T2D odds in treatment arm

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Figure 4 - Effect of statin therapy on body weight (kg) in 15 trials (random effects meta-analysis)

NB In the majority of trials, the total number of participants free of T2D at baseline for whom weight data were available was smaller than the total number for whom data were available on T2D.

Higher weight in treatment armLower weight in treatment arm

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