Evidence of heterogeneity in statin-associated type 2 diabetes mellitus risk: A meta-analysis of randomized controlled trials and observational studies


      • Statin users had higher risk of incident type 2 diabetes compared to non-users.
      • Statin-associated risk of diabetes higher among observational studies.
      • Heterogeneity observed by study design and among observational studies.
      • Younger ages and lower cholesterol levels associated with higher incident diabetes.



      To conduct a meta-analysis of statin-associated type 2 diabetes mellitus (T2D) risk among randomized controlled trials (RCTs) and observational studies (OBSs), excluding studies conducted among secondary prevention populations.


      Studies were identified by searching PubMed (1994-present) and EMBASE (1994-present). Articles had to meet the following criteria: (1) follow-up >one year; (2) >50% of participants free of clinically diagnosed ASCVD; (3) adult participants ≥30 years old; (4) reported statin-associated T2D effect estimates; and (5) quantified precision using 95% confidence interval. Data were pooled using random-effects model.


      We identified 23 studies (35% RCTs) of n = 4,012,555 participants. OBS participants were on average younger (mean difference = 6.2 years) and had lower mean low-density lipoprotein cholesterol (LDL-C, mean difference = 20.6 mg/dL) and mean fasting plasma glucose (mean difference = 5.2 mg/dL) compared to RCT participants. There was little evidence for publication bias (P > 0.1). However, evidence of heterogeneity was observed overall and among OBSs and RCTs (PCochran = <0.05). OBS designs, younger baseline mean ages, lower LDL-C concentrations, and high proportions of never or former smokers were significantly associated with increased statin-associated T2D risk.


      Potentially elevated statin-associated T2D risk in younger populations with lower LDL-C merits further investigation in light of evolving statin guidelines targeting primary prevention populations.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Diabetes Research and Clinical Practice
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


      1. Gu Q, Paulose-Ram R, Burt V, Kit B. Prescription cholesterol-lowering medication use in adults aged 40 and over: United States, 2003–2012. NCHS data brief, no 177. Hyattsville, MD: National Center for Health Statistics, US Department of Health and Human Services, CDC; 2014; 2015.

        • Kantor E.D.
        • Rehm C.D.
        • Haas J.S.
        • Chan A.T.
        • Giovannucci E.L.
        Trends in prescription drug use among adults in the United States from 1999–2012.
        JAMA: J Am Med Assoc. 2015; 314: 1818-1830
        • Goff Jr., D.C.
        • Lloyd-Jones D.M.
        • Bennett G.
        • Coady S.
        • D'Agostino Sr., R.B.
        • Gibbons R.
        • et al.
        ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
        J Am Coll Cardiol. 2013; 2014: 2935-2959
        • Stone N.J.
        • Robinson J.G.
        • Lichtenstein A.H.
        • Bairey Merz C.N.
        • Blum C.B.
        • Eckel R.H.
        • et al.
        ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
        Circulation. 2013; 2014: S1-45
        • Trialists C.T.
        The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials.
        The Lancet. 2012; 380: 581-590
        • Trialists C.T.
        Efficacy and safety of LDL-lowering therapy among men and women: meta-analysis of individual data from 174 000 participants in 27 randomised trials.
        The Lancet. 2015; 385: 1397-1405
        • Rajpathak S.N.
        • Kumbhani D.J.
        • Crandall J.
        • Barzilai N.
        • Alderman M.
        • Ridker P.M.
        Statin therapy and risk of developing type 2 diabetes: a meta-analysis.
        Diabetes Care. 2009; 32: 1924-1929
        • Sattar N.
        • Preiss D.
        • Murray H.M.
        • Welsh P.
        • Buckley B.M.
        • de Craen A.J.
        • et al.
        Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials.
        The Lancet. 2010; 375: 735-742
        • Taylor F.
        • Huffman M.D.
        • Macedo A.F.
        • Moore T.H.
        • Burke M.
        • Davey Smith G.
        • et al.
        Statins for the primary prevention of cardiovascular disease.
        Cochrane Library. 2013;
        • Chou R.
        • Tracy Dana M.
        • Blazina I.
        • Daeges M.
        • Jeanne T.L.
        Statins for prevention of cardiovascular disease in adults evidence report and systematic review for the US preventive services task force.
        JAMA: J Am Med Assoc. 2016; 316: 2008-2024
        • Casula M.
        • Mozzanica F.
        • Scotti L.
        • Tragni E.
        • Pirillo A.
        • Corrao G.
        • et al.
        Statin use and risk of new-onset diabetes: a meta-analysis of observational studies.
        Nutr, Metab Cardiovasc Dis. 2017; 27: 396-406
        • Mendis S.
        • Abegunde D.
        • Yusuf S.
        • Ebrahim S.
        • Shaper G.
        • Ghannem H.
        • et al.
        WHO study on Prevention of REcurrences of Myocardial Infarction and StrokE (WHO-PREMISE).
        Bull World Health Organ. 2005; 83: 820-829
        • Lloyd-Jones D.M.
        • Leip E.P.
        • Larson M.G.
        • d’Agostino R.B.
        • Beiser A.
        • Wilson P.W.
        • et al.
        Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age.
        Circulation. 2006; 113: 791-798
        • Shrier I.
        • Boivin J.-F.
        • Steele R.J.
        • Platt R.W.
        • Furlan A.
        • Kakuma R.
        • et al.
        Should meta-analyses of interventions include observational studies in addition to randomized controlled trials? A critical examination of underlying principles.
        Am J Epidemiol. 2007; 166: 1203-1209
        • Faraoni D.
        • Schaefer S.T.
        Randomized controlled trials vs. observational studies: why not just live together?.
        BMC Anesthesiol. 2016; 16: 102
        • Moher D.
        • Liberati A.
        • Tetzlaff J.
        • Altman D.G.
        Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
        Int J Surg. 2010; 8: 336-341
        • Reuters T.
        EndNote X7.
        Thomson Reuters, Philadelphia2013
        • Guyatt G.
        • Rennie D.
        • Meade M.
        • Cook D.
        Users' guides to the medical literature.
        McGraw-Hill Medical, 2015
        • Wilson K.
        • Gibson N.
        • Willan A.
        • Cook D.
        Effect of smoking cessation on mortality after myocardial infarction: meta-analysis of cohort studies.
        Arch Intern Med. 2000; 160: 939-944
        • Austin P.C.
        The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.
        Med Decis Making. 2009; 29: 661-677
        • Sterne J.A.
        • Sutton A.J.
        • Ioannidis J.P.
        • Terrin N.
        • Jones D.R.
        • Lau J.
        • et al.
        Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials.
        BMJ. 2011; 343: d4002
        • Egger M.
        • Smith G.D.
        • Schneider M.
        • Minder C.
        Bias in meta-analysis detected by a simple, graphical test.
        BMJ. 1997; 315: 629-634
        • Begg C.B.
        • Mazumdar M.
        Operating characteristics of a rank correlation test for publication bias.
        Biometrics. 1994; 1088–1101
        • Duval S.
        • Tweedie R.
        Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis.
        Biometrics. 2000; 56: 455-463
        • Cochran W.G.
        The combination of estimates from different experiments.
        Biometrics. 1954; 10: 101-129
        • Furlan A.D.
        • Pennick V.
        • Bombardier C.
        • van Tulder M.
        2009 updated method guidelines for systematic reviews in the Cochrane Back Review Group.
        Spine. 2009; 34: 1929-1941
        • Higgins J.P.
        • Thompson S.G.
        • Deeks J.J.
        • Altman D.G.
        Measuring inconsistency in meta-analyses.
        BMJ Br Med J. 2003; 327: 557
        • Bax L.
        • Ikeda N.
        • Fukui N.
        • Yaju Y.
        • Tsuruta H.
        • Moons K.G.
        More than numbers: the power of graphs in meta-analysis.
        Am J Epidemiol. 2008; 169: 249-255
        • Thompson S.G.
        • Higgins J.P.
        How should meta-regression analyses be undertaken and interpreted?.
        Stat Med. 2002; 21: 1559-1573
        • StataCorp
        Stata statistical software: Release 15.
        StataCorp LLC, College Station, TX2017
        • Menke A.
        • Casagrande S.
        • Geiss L.
        • Cowie C.C.
        Prevalence of and trends in diabetes among adults in the United States, 1988–2012.
        JAMA: J Am Med Assoc. 2015; 314: 1021-1029
        • Magder L.S.
        • Hughes J.P.
        Logistic regression when the outcome is measured with uncertainty.
        Am J Epidemiol. 1997; 146: 195-203
        • Lévesque L.E.
        • Hanley J.A.
        • Kezouh A.
        • Suissa S.
        Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes.
        BMJ. 2010; 340: b5087
        • Higgins J.P.
        • Altman D.G.
        • Gøtzsche P.C.
        • Jüni P.
        • Moher D.
        • Oxman A.D.
        • et al.
        The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials.
        BMJ. 2011; 343: d5928
        • Currie O.
        • Mangin D.
        • Williman J.
        • McKinnon-Gee B.
        • Bridgford P.
        The comparative risk of new-onset diabetes after prescription of drugs for cardiovascular risk prevention in primary care: a national cohort study.
        BMJ Open. 2013; 3: e003475
        • Yola M.
        • Lucien A.
        Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research.
        J Clin Epidemiol. 1994; 47: 731-737
        • Furberg C.D.
        • Wright J.T.
        • Davis B.R.
        • Cutler J.A.
        • Alderman M.
        • Black H.
        • et al.
        Major outcomes in moderately hypercholesterolemic, hypertensive patients randomized to pravastatin vs usual care: the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT).
        JAMA – J Am Med Assoc. 2002; 288: 2998-3007
        • Wilke R.
        • Xu H.
        • Denny J.
        • Roden D.
        • Krauss R.
        • McCarty C.
        • et al.
        The emerging role of electronic medical records in pharmacogenomics.
        Clin Pharmacol Ther. 2011; 89: 379-386
        • Stuart E.A.
        • Bradshaw C.P.
        • Leaf P.J.
        Assessing the generalizability of randomized trial results to target populations.
        Prev Sci. 2015; 16: 475-485
        • Saleheen D.
        • Rader D.J.
        • Voight B.F.
        Disentangling the causal association of plasma lipid traits and type 2 diabetes using human genetics.
        JAMA Cardiol. 2016; 1: 631-633
        • Navarese E.P.
        • Robinson J.G.
        • Kowalewski M.
        • Kołodziejczak M.
        • Andreotti F.
        • Bliden K.
        • et al.
        Association between baseline LDL-C level and total and cardiovascular mortality after LDL-C lowering: a systematic review and meta-analysis.
        JAMA: J Am Med Assoc. 2018; 319: 1566-1579
        • Bibbins-Domingo K.
        • Grossman D.C.
        • Curry S.J.
        • Davidson K.W.
        • Epling J.W.
        • García F.A.
        • et al.
        Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force recommendation statement.
        JAMA: J Am Med Assoc. 2016; 316: 1997-2007
      2. Selby JV, Smith DH, Johnson ES, Raebel MA, Friedman GD, McFarland BH. Kaiser Permanente medical care program. Pharmacoepidemiology, 4th ed. 2005:241–9.

        • Lawlor D.A.
        Commentary: two-sample Mendelian randomization: opportunities and challenges.
        Int J Epidemiol. 2016; 45: 908-915
        • Riley R.D.
        • Lambert P.C.
        • Abo-Zaid G.
        Meta-analysis of individual participant data: rationale, conduct, and reporting.
        BMJ. 2010; 340: c221
        • Cooper H.
        • Patall E.A.
        The relative benefits of meta-analysis conducted with individual participant data versus aggregated data.
        Psychol Methods. 2009; 14: 165
        • Preiss D.
        • Seshasai S.R.K.
        • Welsh P.
        • Murphy S.A.
        • Ho J.E.
        • Waters D.D.
        • et al.
        Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis.
        JAMA: J Am Med Assoc. 2011; 305: 2556-2564
        • Jick S.S.
        • Bradbury B.D.
        Statins and newly diagnosed diabetes.
        Br J Clin Pharmacol. 2004; 58: 303-309
        • Sheiner L.B.
        • Rubin D.B.
        Intention-to-treat analysis and the goals of clinical trials.
        Clin Pharmacol Ther. 1995; 57: 6-15
        • Coutinho M.
        • Gerstein H.C.
        • Wang Y.
        • Yusuf S.
        The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years.
        Diabetes Care. 1999; 22: 233-240