Association of Statin Therapy Initiation With Diabetes Progression: A Retrospective Matched-Cohort Study | Cardiology | JAMA Internal Medicine | JAMA Network
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Table 1.  Baseline Characteristics of Propensity Score–Matched Statin Users and Active Comparators in the Overall and Diabetes-Prevalent Cohorts
Baseline Characteristics of Propensity Score–Matched Statin Users and Active Comparators in the Overall and Diabetes-Prevalent Cohorts
Table 2.  Odds of Outcomes During Follow-up Period Between Statin Users and Active Comparators in Propensity Score–Matched Cohorts
Odds of Outcomes During Follow-up Period Between Statin Users and Active Comparators in Propensity Score–Matched Cohorts
Table 3.  Secondary Analysis and Sensitivity Analysis Comparing the Diabetes Progression Composite Outcome During Follow-up Between Statin Users and Active Comparators
Secondary Analysis and Sensitivity Analysis Comparing the Diabetes Progression Composite Outcome During Follow-up Between Statin Users and Active Comparators
1.
Stone  NJ, Robinson  JG, Lichtenstein  AH,  et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  2013 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.   J Am Coll Cardiol. 2014;63(25 Pt B):2889-2934. doi:10.1016/j.jacc.2013.11.002 PubMedGoogle ScholarCrossref
2.
American Diabetes Association.  Cardiovascular disease and risk management: standards of medical care in diabetes-2018.   Diabetes Care. 2018;41(suppl 1):S86-S104. doi:10.2337/dc18-S009 PubMedGoogle ScholarCrossref
3.
Holman  RR, Paul  S, Farmer  A, Tucker  L, Stratton  IM, Neil  HA; Atorvastatin in Factorial with Omega-3 EE90 Risk Reduction in Diabetes Study Group.  Atorvastatin in Factorial with Omega-3 EE90 Risk Reduction in Diabetes (AFORRD): a randomised controlled trial.   Diabetologia. 2009;52(1):50-59. doi:10.1007/s00125-008-1179-5 PubMedGoogle ScholarCrossref
4.
Erlandson  KM, Jiang  Y, Debanne  SM, McComsey  GA.  Rosuvastatin worsens insulin resistance in HIV-infected adults on antiretroviral therapy.   Clin Infect Dis. 2015;61(10):1566-1572. doi:10.1093/cid/civ554 PubMedGoogle ScholarCrossref
5.
Koh  KK, Quon  MJ, Han  SH, Lee  Y, Kim  SJ, Shin  EK.  Atorvastatin causes insulin resistance and increases ambient glycemia in hypercholesterolemic patients.   J Am Coll Cardiol. 2010;55(12):1209-1216. doi:10.1016/j.jacc.2009.10.053 PubMedGoogle ScholarCrossref
6.
Liew  SM, Lee  PY, Hanafi  NS,  et al.  Statins use is associated with poorer glycaemic control in a cohort of hypertensive patients with diabetes and without diabetes.   Diabetol Metab Syndr. 2014;6:53. doi:10.1186/1758-5996-6-53 PubMedGoogle ScholarCrossref
7.
Sukhija  R, Prayaga  S, Marashdeh  M,  et al.  Effect of statins on fasting plasma glucose in diabetic and nondiabetic patients.   J Investig Med. 2009;57(3):495-499. doi:10.2310/JIM.0b013e318197ec8b PubMedGoogle ScholarCrossref
8.
Cederberg  H, Stančáková  A, Yaluri  N, Modi  S, Kuusisto  J, Laakso  M.  Increased risk of diabetes with statin treatment is associated with impaired insulin sensitivity and insulin secretion: a 6 year follow-up study of the METSIM cohort.   Diabetologia. 2015;58(5):1109-1117. doi:10.1007/s00125-015-3528-5 PubMedGoogle ScholarCrossref
9.
Ridker  PM, MacFadyen  J, Cressman  M, Glynn  RJ.  Efficacy of rosuvastatin among men and women with moderate chronic kidney disease and elevated high-sensitivity C-reactive protein: a secondary analysis from the JUPITER (Justification for the Use of Statins in Prevention-an Intervention Trial Evaluating Rosuvastatin) trial.   J Am Coll Cardiol. 2010;55(12):1266-1273. doi:10.1016/j.jacc.2010.01.020 PubMedGoogle ScholarCrossref
10.
Kilpatrick  ES, Rigby  AS, Atkin  SL.  Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: “double diabetes” in the Diabetes Control and Complications Trial.   Diabetes Care. 2007;30(3):707-712. doi:10.2337/dc06-1982 PubMedGoogle ScholarCrossref
11.
Brownlee  M.  The pathobiology of diabetic complications: a unifying mechanism.   Diabetes. 2005;54(6):1615-1625. doi:10.2337/diabetes.54.6.1615 PubMedGoogle ScholarCrossref
12.
Maki  KC, Ridker  PM, Brown  WV, Grundy  SM, Sattar  N; The Diabetes Subpanel of the National Lipid Association Expert Panel.  An assessment by the Statin Diabetes Safety Task Force: 2014 update.   J Clin Lipidol. 2014;8(3)(suppl):S17-S29. doi:10.1016/j.jacl.2014.02.012 PubMedGoogle ScholarCrossref
13.
Health Services Research and Development. Accessed October 22, 2020. https://www.hsrd.research.va.gov/for_researchers/vinci/cdw.cfm
14.
Miller  DR, Safford  MM, Pogach  LM.  Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data.   Diabetes Care. 2004;27(suppl 2):B10-B21. doi:10.2337/diacare.27.suppl_2.B10 PubMedGoogle ScholarCrossref
15.
Alvarez  CA, Halm  EA, Pugh  MJV,  et al.  Lactic acidosis incidence with metformin in patients with type 2 diabetes and chronic kidney disease: A retrospective nested case-control study.   Endocrinol Diabetes Metab. 2020;4(1):e00170. doi:10.1002/edm2.170 PubMedGoogle Scholar
16.
Lund  JL, Richardson  DB, Stürmer  T.  The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application.   Curr Epidemiol Rep. 2015;2(4):221-228. doi:10.1007/s40471-015-0053-5 PubMedGoogle ScholarCrossref
17.
Yoshida  K, Solomon  DH, Kim  SC.  Active-comparator design and new-user design in observational studies.   Nat Rev Rheumatol. 2015;11(7):437-441. doi:10.1038/nrrheum.2015.30 PubMedGoogle ScholarCrossref
18.
Danaei  G, Rodríguez  LA, Cantero  OF, Logan  R, Hernán  MA.  Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease.   Stat Methods Med Res. 2013;22(1):70-96. doi:10.1177/0962280211403603 PubMedGoogle ScholarCrossref
19.
Jee  SH, Sull  JW, Park  J,  et al.  Body-mass index and mortality in Korean men and women.   N Engl J Med. 2006;355(8):779-787. doi:10.1056/NEJMoa054017 PubMedGoogle ScholarCrossref
20.
Allison  DB, Faith  MS, Heo  M, Townsend-Butterworth  D, Williamson  DF.  Meta-analysis of the effect of excluding early deaths on the estimated relationship between body mass index and mortality.   Obes Res. 1999;7(4):342-354. doi:10.1002/j.1550-8528.1999.tb00417.x PubMedGoogle ScholarCrossref
21.
Elixhauser  A, Steiner  C, Palmer  L. Clinical Classifications Software (CCS) for ICD-9-CM. Databases and related tools from the Healthcare Cost and Utilization Project (HCUP): appendix a. 2012. Accessed August 30, 2021. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/AppendixASingleDX.txt
22.
Henderson  DC, Cagliero  E, Copeland  PM,  et al.  Elevated hemoglobin A1c as a possible indicator of diabetes mellitus and diabetic ketoacidosis in schizophrenia patients receiving atypical antipsychotics.   J Clin Psychiatry. 2007;68(4):533-541. doi:10.4088/JCP.v68n0407 PubMedGoogle ScholarCrossref
23.
Wetterhall  SF, Olson  DR, DeStefano  F,  et al.  Trends in diabetes and diabetic complications, 1980-1987.   Diabetes Care. 1992;15(8):960-967. doi:10.2337/diacare.15.8.960 PubMedGoogle ScholarCrossref
24.
Christakis  DA, Feudtner  C, Pihoker  C, Connell  FA.  Continuity and quality of care for children with diabetes who are covered by medicaid.   Ambul Pediatr. 2001;1(2):99-103. doi:10.1367/1539-4409(2001)001<0099:CAQOCF>2.0.CO;2 PubMedGoogle ScholarCrossref
25.
Khokhar  B, Jette  N, Metcalfe  A,  et al.  Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations.   BMJ Open. 2016;6(8):e009952. doi:10.1136/bmjopen-2015-009952 PubMedGoogle Scholar
26.
Saydah  SH, Geiss  LS, Tierney  E, Benjamin  SM, Engelgau  M, Brancati  F.  Review of the performance of methods to identify diabetes cases among vital statistics, administrative, and survey data.   Ann Epidemiol. 2004;14(7):507-516. doi:10.1016/j.annepidem.2003.09.016 PubMedGoogle ScholarCrossref
27.
Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.   J Clin Epidemiol. 1992;45(6):613-619. doi:10.1016/0895-4356(92)90133-8 PubMedGoogle ScholarCrossref
28.
D’Agostino  RB  Sr, Vasan  RS, Pencina  MJ,  et al.  General cardiovascular risk profile for use in primary care: the Framingham Heart Study.   Circulation. 2008;117(6):743-753. doi:10.1161/CIRCULATIONAHA.107.699579 PubMedGoogle ScholarCrossref
29.
Quan  H, Li  B, Saunders  LD,  et al; IMECCHI Investigators.  Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database.   Health Serv Res. 2008;43(4):1424-1441. doi:10.1111/j.1475-6773.2007.00822.x PubMedGoogle ScholarCrossref
30.
Goff  SL, Pekow  PS, Markenson  G, Knee  A, Chasan-Taber  L, Lindenauer  PK.  Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities.   Paediatr Perinat Epidemiol. 2012;26(5):421-429. doi:10.1111/j.1365-3016.2012.01303.x PubMedGoogle ScholarCrossref
31.
Stevens  LA, Coresh  J, Feldman  HI,  et al.  Evaluation of the modification of diet in renal disease study equation in a large diverse population.   J Am Soc Nephrol. 2007;18(10):2749-2757. doi:10.1681/ASN.2007020199 PubMedGoogle ScholarCrossref
32.
Becker  SO, Ichino  A.  Estimation of average treatment effects based on propensity scores.   Stata J. 2002;2(4):358-377. doi:10.1177/1536867X0200200403 Google ScholarCrossref
33.
Leuven  E, Sianesi  B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing, v 4.0.5. 2003. Accessed August 27, 2021. https://ideas.repec.org/c/boc/bocode/s432001.html
34.
Bender  R, Blettner  M.  Calculating the “number needed to be exposed” with adjustment for confounding variables in epidemiological studies.   J Clin Epidemiol. 2002;55(5):525-530. doi:10.1016/S0895-4356(01)00510-8 PubMedGoogle ScholarCrossref
35.
Mansi  IA.  Statins in primary prevention: uncertainties and gaps in randomized trial data.   Am J Cardiovasc Drugs. 2016;16(6):407-418. doi:10.1007/s40256-016-0190-3PubMedGoogle ScholarCrossref
36.
US Centers for Disease Control and Prevention. US diabetes surveillance system and diabetes atlas. Assessed December 9, 2020. https://www.cdc.gov/diabetes/data
37.
Gregg  EW, Hora  I, Benoit  SR.  Resurgence in diabetes-related complications.   JAMA. 2019;321(19):1867-1868. doi:10.1001/jama.2019.3471 PubMedGoogle ScholarCrossref
38.
Rawshani  A, Rawshani  A, Franzén  S,  et al.  Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes.   N Engl J Med. 2018;379(7):633-644. doi:10.1056/NEJMoa1800256 PubMedGoogle ScholarCrossref
39.
Rana  JS, Liu  JY, Moffet  HH, Jaffe  M, Karter  AJ.  Diabetes and prior coronary heart disease are not necessarily risk equivalent for future coronary heart disease events.   J Gen Intern Med. 2016;31(4):387-393. doi:10.1007/s11606-015-3556-3 PubMedGoogle ScholarCrossref
40.
Hu  FB, Stampfer  MJ, Solomon  CG,  et al.  The impact of diabetes mellitus on mortality from all causes and coronary heart disease in women: 20 years of follow-up.   Arch Intern Med. 2001;161(14):1717-1723. doi:10.1001/archinte.161.14.1717 PubMedGoogle ScholarCrossref
41.
Wannamethee  SG, Shaper  AG, Whincup  PH, Lennon  L, Sattar  N.  Impact of diabetes on cardiovascular disease risk and all-cause mortality in older men: influence of age at onset, diabetes duration, and established and novel risk factors.   Arch Intern Med. 2011;171(5):404-410. doi:10.1001/archinternmed.2011.2 PubMedGoogle ScholarCrossref
42.
Idris  I.  Diabetes and cardiovascular risk equivalency: do age at diagnosis and disease duration affect risk stratification?: comment on “impact of diabetes on cardiovascular disease risk and all-cause mortality in older men”.   Arch Intern Med. 2011;171(5):410-411. doi:10.1001/archinternmed.2010.524 PubMedGoogle ScholarCrossref
43.
Colhoun  HM, Betteridge  DJ, Durrington  PN,  et al; CARDS investigators.  Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial.   Lancet. 2004;364(9435):685-696. doi:10.1016/S0140-6736(04)16895-5 PubMedGoogle ScholarCrossref
44.
Swerdlow  DI, Preiss  D, Kuchenbaecker  KB,  et al; DIAGRAM Consortium; MAGIC Consortium; InterAct Consortium.  HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials.   Lancet. 2015;385(9965):351-361. doi:10.1016/S0140-6736(14)61183-1 PubMedGoogle ScholarCrossref
45.
Henriksbo  BD, Lau  TC, Cavallari  JF,  et al.  Fluvastatin causes NLRP3 inflammasome-mediated adipose insulin resistance.   Diabetes. 2014;63(11):3742-3747. doi:10.2337/db13-1398 PubMedGoogle ScholarCrossref
46.
Mitchell  P, Marette  A.  Statin-induced insulin resistance through inflammasome activation: sailing between Scylla and Charybdis.   Diabetes. 2014;63(11):3569-3571. doi:10.2337/db14-1059 PubMedGoogle ScholarCrossref
47.
Buse  MG.  Hexosamines, insulin resistance, and the complications of diabetes: current status.   Am J Physiol Endocrinol Metab. 2006;290(1):E1-E8. doi:10.1152/ajpendo.00329.2005 PubMedGoogle ScholarCrossref
48.
Kernan  WN, Viscoli  CM, Furie  KL,  et al; IRIS Trial Investigators.  Pioglitazone after ischemic stroke or transient ischemic attack.   N Engl J Med. 2016;374(14):1321-1331. doi:10.1056/NEJMoa1506930 PubMedGoogle ScholarCrossref
49.
Mather  KJ, Steinberg  HO, Baron  AD.  Insulin resistance in the vasculature.   J Clin Invest. 2013;123(3):1003-1004. doi:10.1172/JCI67166 PubMedGoogle ScholarCrossref
50.
Semenkovich  CF.  Insulin resistance and atherosclerosis.   J Clin Invest. 2006;116(7):1813-1822. doi:10.1172/JCI29024 PubMedGoogle ScholarCrossref
51.
Smith  AG, Singleton  JR.  Obesity and hyperlipidemia are risk factors for early diabetic neuropathy.   J Diabetes Complications. 2013;27(5):436-442. doi:10.1016/j.jdiacomp.2013.04.003 PubMedGoogle ScholarCrossref
52.
Kissel  JT, Smith  AG.  Understanding small fiber neuropathy: the long and short of it.   JAMA Neurol. 2016;73(6):635-637. doi:10.1001/jamaneurol.2016.0256 PubMedGoogle ScholarCrossref
53.
Verma  A, Visintainer  P, Elarabi  M, Wartak  S, Rothberg  MB.  Overtreatment and undertreatment of hyperlipidemia in the outpatient setting.   South Med J. 2012;105(7):329-333. doi:10.1097/SMJ.0b013e318259bad3 PubMedGoogle ScholarCrossref
54.
Mansi  IA, Frei  CR, Halm  EA, Mortensen  EM.  Association of statins with diabetes mellitus and diabetic complications: role of confounders during follow-up.   J Investig Med. 2017;65(1):32-42. doi:10.1136/jim-2016-000218 PubMedGoogle ScholarCrossref
55.
Raebel  MA, Schroeder  E, Goodrich  G,  et al Validating type 1 and type 2 diabetes mellitus in the Mini-Sentinel Distributed Database using the Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM) DataLink. Accessed on March 4, 2021. https://www.sentinelsystem.org/sentinel/methods/validating-type-1-and-type-2-diabetes-mellitus-mini-sentinel-distributed-database
56.
Wong  ES, Wang  V, Liu  CF, Hebert  PL, Maciejewski  ML.  Do Veterans Health Administration enrollees generalize to other populations?   Med Care Res Rev. 2016;73(4):493-507. doi:10.1177/1077558715617382 PubMedGoogle ScholarCrossref
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    2 Comments for this article
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    Looking at the Whole Picture
    Marc Rendell, M.D. | The Rose Salter Medical Research Foundation and the Association of Diabetes Investigators
    The use of statins has  been associated with decreased cardiovascular mortality. Studies of overall mortality have been somewhat less conclusive (1), but a retrospective cohort study in the Veterans Administration population also using propensity scoring showed a hazard ratio favoring statin use of 0.75 (95% CI, 0.74-0.76) for all-cause mortality, and 0.80 (95% CI, 0.78-0.81) for cardiovascular mortality (2). The study by Mansi et al (3) suggests that statin use was associated with worsening events associated with diabetes. Certainly diabetes is a strong driver of increased mortality. It would be interesting for the authors  to broaden their investigation to look at overall mortality in their population to assure that the findings are not simply related to their propensity scoring approach.

    1) Alla VM, Agrawal V, DeNazareth A, et al.A reappraisal of the risks and benefits of treating to target with cholesterol lowering drugs.Drugs. 2013 Jul;73(10):1025-54

    2) Orkaby AR, Driver JA, Ho Y, et al. Association of Statin Use With All-Cause and Cardiovascular Mortality in US Veterans 75 Years and Older. JAMA. 2020;324(1):68–78.

    3) Mansi IA, Chansard M, Lingvay I, Zhang S, Halm EA, Alvarez CA. Association of Statin Therapy Initiation With Diabetes Progression: A Retrospective Matched-Cohort Study. JAMA Intern Med. Published online October 04, 2021
    CONFLICT OF INTEREST: None Reported
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    Personalizing lipid lowering strategy to balance cardiovascular risk reduction and side effects
    Francesco Sbrana, MD | Lipoapheresis Unit and Reference Center for Inherited Dyslipidemias, Fondazione Toscana Gabriele Monasterio, Pisa, Italy

    We read with great interest the paper by Mansi et al. [1] on the relationship between statin therapy initiation and diabetes progression. In our opinion, this possible causal association needs to be better investigated.

    It is known that the risk of statin-induced newly diagnosed diabetes mellitus is about 0.2% per year of treatment although this estimate is dependent on population risk and will be greater in patients with clinical characteristics associated with higher risk of diabetes mellitus (such as metabolic syndrome) [2]. Furthermore, it is known that simvastatin significantly raises the levels of lipoprotein (a) [3], increasing the patient's atherosclerotic risk. There is the opportunity to measure lipoprotein (a) in people on statin therapy as well as in subjects with high cardiovascular risk or family history of premature atherothrombotic disease.

    Statins possibly increase the risk of hemorrhagic stroke in subjects with previous cerebrovascular disease while they clearly produce a greater reduction in the risk of cardiovascular events and atherothrombotic stroke [2]. In clinical practice about 10% of patients stop taking a statin because of muscle symptoms without raised creatine kinase so as statins may be responsible for tendonitis and cataracts [2].
    Indeed, international guidelines on acute coronary syndromes recommend early administration of high-intensity statins to satisfy "the lower, the faster, the better" concept; the new lipid-lowering therapies, such as Proprotein Convertase Subtilisin Kexin type 9 inhibitors (PCSK9i), allow patients to reach very low LDL-C levels using statins as backbone lipid lowering therapies [4]. Long-term follow-up for PCSK9i is not available, but it has been suggested that in patients with pre‐existing ischemic heart disease already on statin therapy, LDL-C levels do not necessarily need to be lowered below 70 mg/dL [5].

    These reasons underline the importance of personalizing lipid lowering strategy to obtain an optimal balance between cardiovascular risk reduction and side effects related to lipid-lowering therapy.

    Francesco Sbrana – MD 1, Tiziana Sampietro – MD 1, Beatrice Dal Pino – MD 1.

    (1) Lipoapheresis Unit and Reference Center for Inherited Dyslipidemias, Fondazione Toscana Gabriele Monasterio, Pisa, Italy

    References
    1. Mansi IA, Chansard M, Lingvay I, et al. Association of statin therapy initiation with diabetes progression: a retrospective matched-cohort study. JAMA Intern Med. 2021 doi: 10.1001/jamainternmed.2021.5714.
    2. Newman CB, Preiss D, Tobert JA, et al. Statin safety and associated adverse events: a scientific statement from the American Heart Association. Arterioscler Thromb Vasc Biol. 2019; 39: e38-e81.
    3. Sampietro T, Galetta F, Bionda A. Behavior of Lp(a) and apoproteins (A1, B, C2, C3, E) during and after therapy with simvastatin. Cardiovasc Drugs Ther. 1995; 9: 785-789.
    4. Gencer B, Mach F. Lipid management in ACS: Should we go lower faster? Atherosclerosis. 2018; 275: 368-375.
    5. Leibowitz M, Karpati T, Cohen-Stavi CJ, et al. Association between achieved low-density lipoprotein levels and major adverse cardiac events in patients with stable ischemic heart disease taking statin treatment. JAMA Int. Med. 2016; 176: 1105-1113.

    CONFLICT OF INTEREST: None Reported
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    Original Investigation
    October 4, 2021

    Association of Statin Therapy Initiation With Diabetes Progression: A Retrospective Matched-Cohort Study

    Author Affiliations
    • 1Department of Medicine, VA North Texas Health Care System, Dallas
    • 2Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas
    • 3Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas
    • 4Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas
    • 5Department of Pharmacy Practice, Texas Tech University Health Sciences Center, Dallas
    JAMA Intern Med. Published online October 4, 2021. doi:10.1001/jamainternmed.2021.5714
    Key Points

    Question  What is the association of statin treatment initiation and diabetes progression in patients with diabetes?

    Findings  This large retrospective cohort study included 83 022 propensity-scored matched pairs of statin users and nonusers and found that the diabetes-progression composite outcome was significantly higher among patients with diabetes who used statins than among patients with diabetes who did not use statins. The study examined 12 years of data on patients covered by the Veterans Affairs health system and new-user and active-comparator designs to assess associations between statin initiation and diabetes progression from 2003 to 2015.

    Meaning  Statin use was associated with diabetes progression in patients with diabetes—statin users had a higher likelihood of insulin treatment initiation, developing significant hyperglycemia, experiencing acute glycemic complications, and being prescribed an increased number of glucose-lowering medication classes.

    Abstract

    Importance  Statin therapy has been associated with increased insulin resistance; however, its clinical implications for diabetes control among patients with diabetes is unknown.

    Objective  To assess diabetes progression after initiation of statin use in patients with diabetes.

    Design, Setting, and Participants  This was a retrospective matched-cohort study using new-user and active-comparator designs to assess associations between statin initiation and diabetes progression in a national cohort of patients covered by the US Department of Veterans Affairs from fiscal years 2003-2015. Patients included were 30 years or older; had been diagnosed with diabetes during the study period; and were regular users of the Veterans Affairs health system, with records of demographic information, clinical encounters, vital signs, laboratory data, and medication usage.

    Interventions  Treatment initiation with statins (statin users) or with H2-blockers or proton pump inhibitors (active comparators).

    Main Outcomes and Measures  Diabetes progression composite outcome comprised the following: new insulin initiation, increase in the number of glucose-lowering medication classes, incidence of 5 or more measurements of blood glucose of 200 mg/dL or greater, or a new diagnosis of ketoacidosis or uncontrolled diabetes.

    Results  From the 705 774 eligible patients, we matched 83 022 pairs of statin users and active comparators; the matched cohort had a mean (SD) age of 60.1 (11.6) years; 78 712 (94.9%) were men; 1715 (2.1%) were American Indian/Pacific Islander/Alaska Native, 570 (0.8%) were Asian, 17 890 (21.5%) were Black, and 56 633 (68.2 %) were White individuals. Diabetes progression outcome occurred in 55.9% of statin users vs 48.0% of active comparators (odds ratio, 1.37; 95% CI, 1.35-1.40; P < .001). Each individual component of the composite outcome was significantly higher among statin users. Secondary analysis demonstrated a dose-response relationship with a higher intensity of low-density lipoprotein-cholesterol lowering associated with greater diabetes progression.

    Conclusions and Relevance  This retrospective matched-cohort study found that statin use was associated with diabetes progression, including greater likelihood of insulin treatment initiation, significant hyperglycemia, acute glycemic complications, and an increased number of prescriptions for glucose-lowering medication classes. The risk-benefit ratio of statin use in patients with diabetes should take into consideration its metabolic affects.

    Introduction

    Guidelines recommend statin therapy for all patients with diabetes mellitus type 2 (diabetes) who are 40 to 75 years old and have a low-density lipoprotein (LDL) cholesterol level of 70 mg/dL or greater (to convert to mmol/L, multiply by 0.0259) for primary prevention of cardiovascular diseases (CVD).1,2 However, statin use has been associated with increased insulin resistance and higher blood glucose levels. Several randomized controlled trials (RCTs)3-5 and large prospective and retrospective observational studies6-8 have noted that patients treated with statin therapy (hereafter, statin users) had increased insulin resistance, hemoglobin A1C (HbA1C) levels, and fasting plasma glucose levels. In a large observational study, statin use was associated with a 24% reduction in insulin sensitivity.8 Despite the significant reduction in insulin sensitivity and the increase in fasting plasma insulin, the difference in fasting plasma glucose and HbA1C between statin users and nonusers appears modest.8 For example, a large RCT noted that an increase in HbA1C was 0.30% in the rosuvastatin group and 0.22% in the placebo group (P < .001), and there was no significant difference in fasting serum glucose between the groups.9

    A modest change in fasting blood glucose after statin initiation despite a relatively large change in insulin resistance and fasting insulin levels deserves further study. Clinicians might escalate antidiabetes therapy to offset the rising blood glucose; hence, HbA1C levels may underestimate how statin therapy influences diabetes control. Yet increased insulin resistance is of concern because it may fuel diabetes disease progression.10,11 It is important to understand the clinical importance of increased insulin resistance to actual patient care.

    The statin Diabetes Safety Task Force has remarked on the paucity of data regarding how statin use affects glycemic control.12 The present study’s objective was to compare diabetes progression (by assessing new insulin treatment initiations, changes in the number of glucose-lowering medication classes, and new persistent hyperglycemia or acute glycemic complications) after statin initiation with progression among nonusers in a national cohort of patients covered by the US Department of Veterans Affairs (VA).

    Methods
    Study Design

    This was a retrospective matched-cohort study that used new-user and active-comparator designs to assess associations between statin initiation and diabetes progression among a national cohort of patients covered by the VA from fiscal year (FY) 2003-FY 2015. The study was reviewed and approved by the institutional review boards of the VA North Texas Health Care System and the Texas Tech University Health Sciences Center. Informed consent was waived because the study used only preexisting deidentified data. The study followed the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline and the reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiologic research (RECORD-PE).

    We extracted medical record data from the national VA Corporate Data Warehouse FY 2003-FY 2015 (October 1, 2002-September 30, 2015), which captures several domains under published protocols.13 It includes inpatient and outpatient diagnoses and procedure codes, pharmacy and medication usage, vital signs records, and laboratory data. We used all available medical encounters for a national VA cohort of patients diagnosed with diabetes during the study period, identified using a validated algorithm.14,15

    The overall cohort included patients who met these criteria, were age 30 years or older at the index date and were regular VA health system users. We defined regular VA system users as having the following during both the baseline and follow-up periods: (1) at least 1 VA encounter; (2) blood pressure and weight measurements; (3) VA pharmacy dispensing; and (4) laboratory data, including blood or serum glucose, creatinine, and LDL cholesterol measurements.

    Study Groups

    We used an active comparator, new user design, to minimize unmeasured confounders and confounding by indication.16,17 The statin user group was composed of patients who initiated statin therapy within the study period. The active comparator group was composed of patients who initiated an H2-blocker or proton pump inhibitor (H2/PPI) and were not concurrently prescribed a statin. To exclude prevalent users, any patient who during the 12 months prior to cohort entry had filled a statin prescription was excluded from the statin user group and any patient who had filled an H2-blocker or PPI prescription was excluded from the active comparator group. For any patient in the active comparator group who filled a statin prescription during the follow-up period, their follow-up ended as a nonuser (on the date of statin initiation) and they were crossed over to the statin user group (the date of statin initiation became their new index date). This design mitigated confounding by indication and immortal time bias.18

    The index date was the date on which the first prescription for statin therapy or an H2 or PPI was filled. Because the study data included all available encounters from FY 2003-FY 2015, regardless of when a patient was diagnosed with diabetes, the index date could precede, coincide with, or occur after the date of diabetes diagnosis.

    Study Intervals

    The baseline period, which was used to describe baseline characteristics, comprised the year preceding the index date. The follow-up period, which was used to ascertain outcomes, started at the index date and continued until: (1) the last date of VA care, (2) the end of the study period, (3) death of the patient, or (4) date of statin initiation in active comparators who subsequently used a statin. To minimize confounding, we excluded patients who had fewer than 60 days of follow-up because the study outcomes would be highly unlikely to occur with fewer than 60 days of statin exposure.18-20

    Primary Outcome—Diabetes Progression

    This study’s prespecified outcome was a dichotomous composite outcome that comprised (1) therapy intensification, including new insulin initiation during the follow-up period or an increased number of glucose-lowering medication classes that were ever used during follow-up in comparison with the baseline (eTable 1 in the Supplement) and (2) new persistent hyperglycemia or acute glycemic complications, including: (a) the presence of 5 or more measurements with blood glucose levels of 200 mg/dL or greater (to convert to mmol/L, multiply by 0.0555) during follow-up (not present during the baseline period) and (b) receiving a new diagnosis of diabetes with ketoacidosis or uncontrolled diabetes during the follow-up period (not present during the baseline period). The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes were used to identify diabetic ketoacidosis or uncontrolled diabetes (eTable 2 in the Supplement)21 and have been widely used in the literature.22-24 Overall, administrative health databases have found ICD-9-CM codes to be useful for identifying diabetes and its complications, with specificity from 94.3% to 100%, although sensitivity can vary.25,26

    Secondary Outcomes

    The study had 4 secondary outcomes. The first was individual components of diabetes progression outcome; second, the difference in the number of glucose-lowering medication classes ever used during the follow-up period by each individual in comparison with the number during their baseline; third, the proportion of patients with a decreased number of glucose-lowering medication classes during follow-up vs baseline; and fourth, the change in mean blood glucose (mg/dL) during follow-up vs baseline.

    Cohort Characterization

    Patients’ comorbidities were identified using ICD-9-CM codes as defined by the Agency for Health Research and Quality Clinical Classifications Software disease categories.21 We calculated each patient’s Charlson comorbidity index27 score and cardiovascular risk (eTables 3 and 4 in the Supplement).28-30 We created a propensity score (PS) to match statin users and active comparators (nonusers) at a ratio of 1:1 using 93 variables chosen a priori (Table 1).31 We used the routine by Leuven and Sianesi32 to perform multivariable logistic regression to estimate the PS and to perform nearest number matching using the logit model.33 We subsequently examined the balance in baseline characteristics between treatment groups. A caliper of 0.00014 was found to balance differences between treatment groups and maximize sample size (eMethods in the Supplement).

    Primary and Secondary Analyses

    In the primary analysis, we compared the primary and secondary outcomes in the PS matched cohort using conditional logistic regression to calculate odds ratios (OR) and 95% CI. For the secondary analyses, we compared the primary outcome in the following cohorts:

    • 1. Overall cohort: all patients who fulfilled inclusion and exclusion criteria of the study before PS matching.

    • 2. Healthy cohort: patients with no comorbidities in the Charlson comorbidity index at baseline.

    • 3. High-intensity cholesterol-lowering statin users (decrease of ≥50% in mean LDL cholesterol during follow-up vs baseline) compared with nonusers in the overall cohort.1

    • 4. Moderate-intensity cholesterol lowering statin users (decrease of <50% and ≥30% in mean LDL cholesterol during follow-up vs baseline) compared with nonusers in the overall cohort.

    • 5. Low-intensity cholesterol lowering statin users (a decrease of <30% in mean LDL cholesterol during follow-up vs baseline) compared with nonusers in the overall cohort.

    • 6. Ever user vs never user cohort: we excluded patients who started as active comparators and were crossed over to the statin user group.

    Sensitivity Analysis

    We excluded patients who were diagnosed with incident diabetes, diabetic complications, ketoacidosis or uncontrolled diabetes, or CVD events within 60 days from the index date. Because it is highly unlikely that statin use would influence these outcomes within 60 days of statin initiation, excluding these patients further mitigated confounding by indication or residual confounding.18-20

    Post Hoc Analysis

    We created another PS matched cohort that included only patients who had diabetes during the baseline period (PS matched prevalent diabetes cohort). All variables and techniques that were used in the primary analysis were included in this cohort. A caliper of 0.00002 was used to achieve balance-maximizing sample size (Table 1).

    Statistical Analysis

    Dichotomous variables were compared using χ2, and continuous variables were compared using t tests. When the Kolmogorov-Smirnov test indicated unequal distribution, we used the Wilcoxon Mann-Whitney test. Statistical tests were 2-tailed, and significance was defined as P < .05.

    Because multiple comparisons in the secondary analyses may produce a type I error, findings of secondary analyses should be interpreted as exploratory. Secondary and sensitivity analyses were performed using a separate logistic regression model for each dichotomous outcome adjusting for the PS. Data management and statistical analyses were conducted from September 2019 to October 2020 using STATA, version 15 (Stata Corp LLC).

    Results

    From the 705 774 eligible VA patients (595 579 statin users and 110 195 nonusers), we matched 83 022 pairs of statin users and active comparators; the matched cohort had a mean (SD) age of 60.1 (11.6) years; 78 712 (94.9%) were men; 1715 (2.1%) were American Indian/Pacific Islander/Alaska Native, 570 (0.8%) were Asian, 17 890 (21.5%) were Black, and 56 633 (68.2 %) were White individuals. To form the study cohort, we had excluded 441 778 patients who were prevalent users of statins, H2, or PPI; 6613 who were less than 30 years old; 36 085 whose follow-up period had been fewer than 60 days; 421 170 who were not regular VA users; and 1098 whose records were missing data on age (eFigure in the Supplement).

    Statin users had filled prescriptions for statins for a mean (SD) duration of 5.3 (3.3) years; median (IQR) of 5.1 (2.6-8.0) years. Statin users filled 12 118 523 prescriptions for statins throughout the study; 63.4% of the prescriptions were for simvastatin, 12.4% for atorvastatin, 10.5% for rosuvastatin, and 9.5% for pravastatin. Among nonusers, 52 176 patients (47.4%) subsequently used a statin during the study period and 58 019 (52.7%) never used a statin.

    Among the 83 022 matched pairs of statin users and nonusers (Table 1) there was only a small difference in race and ethnicity distribution. Based on the cohort characteristics, all patients had been diagnosed with diabetes by the end of the study period. At baseline, the statin user and nonuser groups in the PS matched cohort had similar proportions of patients with diabetes, diabetes complications, recurrent episodes of high blood glucose (≥200 mg/dL), and using glucose-lowering agents; they also had similar mean blood glucose levels and patient follow-up periods. During the follow-up period, the statin users decreased their mean LDL cholesterol by a mean (SD) of 25 (31.6) mg/dL compared with a 0.8 (23.7) mg/dL among nonusers (95% CI of mean difference, 23.9-24.4; P < .001), demonstrating that statin users had effectively used, not just filled, their statin prescriptions (eTable 5 in the Supplement). The mean (SD) number of outpatient encounters by the PS matched cohort during the follow-up period was 37.8 (43.1) for the statin user group and 41.4 (50.5) encounters for the nonuser group.

    Primary Analysis

    Statin users had significantly higher odds of diabetes progression (OR, 1.37; 95% CI, 1.35-1.40) compared with nonusers. There was significantly higher rate of each component of the diabetes progression outcome in statin users compared with nonusers (Table 2) including an increase in the number of glucose-lowering medication classes (OR, 1.41; 95% CI, 1.38-1.43), new insulin starts (OR, 1.16; 95% CI, 1.12-1.19), presence of persistent hyperglycemia (OR, 1.13; 95% CI, 1.10-1.16), and new diagnosis of ketoacidosis or uncontrolled diabetes (OR, 1.24; 95% CI, 1.19-1.30).

    Secondary Analysis

    Odds of diabetes progression outcome were consistently higher in statin users compared with nonusers among all secondary and sensitivity analyses (Table 3). The odds of diabetes progression among statin users in the healthy cohort were higher than the overall cohort (OR, 1.56 vs 1.40) (Table 3). Intensive cholesterol lowering was associated with highest odds of diabetes progression outcome among statin users in compared with nonusers.

    Post Hoc Analysis

    In the PS-matched prevalent diabetes cohort, we matched 51 467 pairs of statin users and nonusers with only a small difference in proportion of men and some racial and ethnic minority subgroups (Table 1). The odds of the primary and secondary outcomes were significantly higher among statin users (Table 2).

    Discussion

    This study of a national cohort of VA patients with diabetes found that statin use was associated with an escalation of diabetes treatment, including a higher risk of initiating insulin and the use of more glucose-lowering medication classes. This escalation of diabetes treatment was associated with worse diabetes control, including new persistent hyperglycemia and acute glycemic complications. Moreover, there was a dose-response association between intensity of lowering LDL cholesterol and risk of the study outcomes, with higher intensity of LDL cholesterol-lowering associated with higher odds of diabetes progression. For example, the odds of diabetes progression among statin users vs nonusers were 1.83, 1.55, and 1.45 for high-, moderate-, and low-intensity cholesterol lowering, respectively.

    Using the Bender and Blettner formula,34 the number needed to be exposed to statins for 1 additional person to experience diabetes progression outcome was 13. Although the mean difference between blood glucose during follow-up and baseline among statin users in contrast with nonusers was modest, there was significant escalation in diabetes therapy that was not associated with better clinical outcomes. This statin-associated metabolic cost was not measured by the RCTs, which instead focused mainly on cardiovascular benefits.35 From 2009 to 2015, annual emergency department visits for hyperglycemic crisis almost doubled, hospitalization increased by 73%, and related deaths increased by 55%.36 This resurgence in diabetes-specific complications deserves attention and a call to scrutinize our practices and goals.37

    The higher risk of diabetes progression associated with statin use may seem less consequential, at least in the short and intermediate term, than the cardiovascular benefits of statin use, especially when used for secondary prevention. However, diabetes progression has long-term effects on quality of life and treatment burden, which warrant consideration when discussing the overall risk-benefit profile, especially when used for primary prevention. In this study, approximately 77% of the cohort had no known CVD at baseline. A recent cohort study38 reported that patients with diabetes with 5 risk factors within a target control range (eg, LDL cholesterol <97 mg/dL) had little or no excess risk of CVD outcomes. Of note, 61.5% of patients in this cohort used statins, and HbA1C was a stronger predictor than LDL cholesterol of risk of death from any cause. Moreover, some studies39-42 have demonstrated that only patients who have had diabetes for at least 10 to 15 years have an increased risk of CVD events. In 1 of these studies,39 only 31% of patients with diabetes and 66% of those with diabetes and coronary heart disease used statins at baseline.

    Several RCTs,3,4,43 Mendelian randomization studies,44 observational studies,5 and animal studies45,46 have demonstrated that statin use increases insulin resistance. The association of statin use with diabetes progression may be explained by its effect on insulin resistance (eDiscussion in the Supplement). Insulin resistance has been shown to increase the risk of diabetic complications,10,47 endothelial dysfunction, inflammation, and increased platelet reactivity.48-52

    Strengths and Limitations

    Strengths of this study are its large size and longitudinal follow-up period with detailed clinical data. Also of note, patients in the nonuser group did not receive statin therapy despite guidelines recommending statins, a finding consistent with other studies53 that indicate that, in community practice, many patients do not receive statins according to guidelines. The present study’s definitions of the intensity of cholesterol lowering is guided by, but not identical to, the definitions used by the American College of Cardiology/American Heart Association (ACC/AHA) and the American Diabetes Association.1 The ACC/AHA identifies high-intensity statins based on their expected efficacy in lowering LDL cholesterol by 50% or more. For this study, we defined intensive cholesterol lowering among statin users as decreasing mean LDL cholesterol during the follow-up period by 50% or more compared with the baseline mean. Similarly, our definitions for low- and moderate-intensity cholesterol lowering used the same cutoff limits as the ACC/AHA guidelines but used the actual decrease in mean LDL cholesterol during follow-up. Our approach incorporates a measure of adherence to treatment, not simply filling of prescriptions.

    This study also had some limitations inherent to retrospective observational data; hence, there is always a chance of unmeasured residual confounding. It may be argued that statin users have closer follow-up, resulting in ascertainment bias. However, the outpatient encounters of the PS matched cohort during the follow-up period were not more frequent among statin users than among nonusers. Prior studies54 have shown that adjusting to number of encounters that took place during follow-up period did not affect outcomes if baseline characteristics were adequately described.

    It could be argued that the physicians who prescribed statins may have also attempted to aggressively control diabetes by adding more glucose-lowering agents; however, this would not explain the increased persistent hyperglycemia nor the ketoacidosis, which would be expected to decrease. It may also be argued that confounding by indication may have played a part in the study findings, specifically in patients with diabetic complications who might have been treated with statins and more aggressive antidiabetes therapy. However, the sensitivity analysis, which excluded patients who experienced any diabetic complications, ketoacidosis, uncontrolled diabetes, or CVD within fewer than 60 days from the index date, demonstrated an association of statin use with diabetes progression, with odds similar those of the primary analysis. We also could not ascertain whether the association of statin use with diabetes progression was because of the statin use or the lower LDL cholesterol—that is, statins are inseparable from their cholesterol-lowering effect. The definition of the study’s composite outcome also had some limitations; an increase in the number of glucose-lowering agents assumed that all agents had similar potency. Also, selecting 5 or more episodes of blood glucose seems arbitrary; nevertheless, the outcome was applied equally to both statin users and nonusers and all components of the composite outcome were directionally the same. The study data could not reliably differentiate between diabetes types because there is no reliable algorithm to accomplish this in a data set such as this,55 and regardless, there is no reason to believe that it would have differentially affected statin users vs nonusers. We expect that the extensive matching of the 2 groups on baseline characteristics, including microvascular and macrovascular diabetes complications, may have acted as a surrogate for diabetes severity and chronicity. Patients covered by the VA were predominantly men, which may limit generalization; however, research has shown that men covered by the VA have characteristics similar to those of individuals covered by other insurance.56 Lastly, the study protocol was not prepublished.

    Conclusions

    This large retrospective matched-cohort study found that statin use was associated with a higher risk of diabetes treatment escalation and an increased risk of hyperglycemic complications. This metabolic cost was not considered in RCTs of statins. Further research is needed to form a risk-tailored approach to balancing the cardiovascular benefits of statin therapy with its risk of diabetes progression.

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    Article Information

    Accepted for Publication: October 4, 2021.

    Published Online: October 4, 2021. doi:10.1001/jamainternmed.2021.5714

    Corresponding Author: Ishak A. Mansi, MD, VA North Texas Health System, 4500 S Lancaster Rd, #111E, Dallas, TX 75216 (ishak.mansi@va.gov).

    Author Contributions: Dr Mansi, Mr Chansard, and Dr Alvarez had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Mansi, Lingvay, Halm, Alvarez.

    Acquisition, analysis, or interpretation of data: Mansi, Chansard, Lingvay, Zhang, Alvarez.

    Drafting of the manuscript: Mansi, Alvarez.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Mansi, Zhang, Alvarez.

    Administrative, technical, or material support: Mansi.

    Supervision: Mansi, Halm, Alvarez.

    Other−data management: Chansard.

    Conflict of Interest Disclosures: Dr Lingvay reports research funding, advisory and consulting fees, and/or other support from Novo Nordisk, Eli Lilly, Sanofi, AstraZeneca, Boehringer Ingelheim, Janssen, Intercept, Intarcia, TARGETPharma, Merck, Pfizer, Novartis, GI Dynamics, Mylan, Mannkind, Valeritas, Bayer, and Zealand Pharma. Dr Alvarez reports a grant (No. K08 DK101602) from the National Institutes of Diabetes and Digestive and Kidney Diseases during the conduct of the study and grants from Merck outside the submitted work. No other disclosures were reported.

    Disclaimer: This material is the result of work supported with resources and the use of facilities at the North Texas VA and University of Texas Southwestern. The views expressed herein are those of the authors and do not reflect the official policy or position of the US Department of the Army, US Department of Defense, the US Department of Veterans Affairs, or the US government.

    References
    1.
    Stone  NJ, Robinson  JG, Lichtenstein  AH,  et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  2013 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.   J Am Coll Cardiol. 2014;63(25 Pt B):2889-2934. doi:10.1016/j.jacc.2013.11.002 PubMedGoogle ScholarCrossref
    2.
    American Diabetes Association.  Cardiovascular disease and risk management: standards of medical care in diabetes-2018.   Diabetes Care. 2018;41(suppl 1):S86-S104. doi:10.2337/dc18-S009 PubMedGoogle ScholarCrossref
    3.
    Holman  RR, Paul  S, Farmer  A, Tucker  L, Stratton  IM, Neil  HA; Atorvastatin in Factorial with Omega-3 EE90 Risk Reduction in Diabetes Study Group.  Atorvastatin in Factorial with Omega-3 EE90 Risk Reduction in Diabetes (AFORRD): a randomised controlled trial.   Diabetologia. 2009;52(1):50-59. doi:10.1007/s00125-008-1179-5 PubMedGoogle ScholarCrossref
    4.
    Erlandson  KM, Jiang  Y, Debanne  SM, McComsey  GA.  Rosuvastatin worsens insulin resistance in HIV-infected adults on antiretroviral therapy.   Clin Infect Dis. 2015;61(10):1566-1572. doi:10.1093/cid/civ554 PubMedGoogle ScholarCrossref
    5.
    Koh  KK, Quon  MJ, Han  SH, Lee  Y, Kim  SJ, Shin  EK.  Atorvastatin causes insulin resistance and increases ambient glycemia in hypercholesterolemic patients.   J Am Coll Cardiol. 2010;55(12):1209-1216. doi:10.1016/j.jacc.2009.10.053 PubMedGoogle ScholarCrossref
    6.
    Liew  SM, Lee  PY, Hanafi  NS,  et al.  Statins use is associated with poorer glycaemic control in a cohort of hypertensive patients with diabetes and without diabetes.   Diabetol Metab Syndr. 2014;6:53. doi:10.1186/1758-5996-6-53 PubMedGoogle ScholarCrossref
    7.
    Sukhija  R, Prayaga  S, Marashdeh  M,  et al.  Effect of statins on fasting plasma glucose in diabetic and nondiabetic patients.   J Investig Med. 2009;57(3):495-499. doi:10.2310/JIM.0b013e318197ec8b PubMedGoogle ScholarCrossref
    8.
    Cederberg  H, Stančáková  A, Yaluri  N, Modi  S, Kuusisto  J, Laakso  M.  Increased risk of diabetes with statin treatment is associated with impaired insulin sensitivity and insulin secretion: a 6 year follow-up study of the METSIM cohort.   Diabetologia. 2015;58(5):1109-1117. doi:10.1007/s00125-015-3528-5 PubMedGoogle ScholarCrossref
    9.
    Ridker  PM, MacFadyen  J, Cressman  M, Glynn  RJ.  Efficacy of rosuvastatin among men and women with moderate chronic kidney disease and elevated high-sensitivity C-reactive protein: a secondary analysis from the JUPITER (Justification for the Use of Statins in Prevention-an Intervention Trial Evaluating Rosuvastatin) trial.   J Am Coll Cardiol. 2010;55(12):1266-1273. doi:10.1016/j.jacc.2010.01.020 PubMedGoogle ScholarCrossref
    10.
    Kilpatrick  ES, Rigby  AS, Atkin  SL.  Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: “double diabetes” in the Diabetes Control and Complications Trial.   Diabetes Care. 2007;30(3):707-712. doi:10.2337/dc06-1982 PubMedGoogle ScholarCrossref
    11.
    Brownlee  M.  The pathobiology of diabetic complications: a unifying mechanism.   Diabetes. 2005;54(6):1615-1625. doi:10.2337/diabetes.54.6.1615 PubMedGoogle ScholarCrossref
    12.
    Maki  KC, Ridker  PM, Brown  WV, Grundy  SM, Sattar  N; The Diabetes Subpanel of the National Lipid Association Expert Panel.  An assessment by the Statin Diabetes Safety Task Force: 2014 update.   J Clin Lipidol. 2014;8(3)(suppl):S17-S29. doi:10.1016/j.jacl.2014.02.012 PubMedGoogle ScholarCrossref
    13.
    Health Services Research and Development. Accessed October 22, 2020. https://www.hsrd.research.va.gov/for_researchers/vinci/cdw.cfm
    14.
    Miller  DR, Safford  MM, Pogach  LM.  Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data.   Diabetes Care. 2004;27(suppl 2):B10-B21. doi:10.2337/diacare.27.suppl_2.B10 PubMedGoogle ScholarCrossref
    15.
    Alvarez  CA, Halm  EA, Pugh  MJV,  et al.  Lactic acidosis incidence with metformin in patients with type 2 diabetes and chronic kidney disease: A retrospective nested case-control study.   Endocrinol Diabetes Metab. 2020;4(1):e00170. doi:10.1002/edm2.170 PubMedGoogle Scholar
    16.
    Lund  JL, Richardson  DB, Stürmer  T.  The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application.   Curr Epidemiol Rep. 2015;2(4):221-228. doi:10.1007/s40471-015-0053-5 PubMedGoogle ScholarCrossref
    17.
    Yoshida  K, Solomon  DH, Kim  SC.  Active-comparator design and new-user design in observational studies.   Nat Rev Rheumatol. 2015;11(7):437-441. doi:10.1038/nrrheum.2015.30 PubMedGoogle ScholarCrossref
    18.
    Danaei  G, Rodríguez  LA, Cantero  OF, Logan  R, Hernán  MA.  Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease.   Stat Methods Med Res. 2013;22(1):70-96. doi:10.1177/0962280211403603 PubMedGoogle ScholarCrossref
    19.
    Jee  SH, Sull  JW, Park  J,  et al.  Body-mass index and mortality in Korean men and women.   N Engl J Med. 2006;355(8):779-787. doi:10.1056/NEJMoa054017 PubMedGoogle ScholarCrossref
    20.
    Allison  DB, Faith  MS, Heo  M, Townsend-Butterworth  D, Williamson  DF.  Meta-analysis of the effect of excluding early deaths on the estimated relationship between body mass index and mortality.   Obes Res. 1999;7(4):342-354. doi:10.1002/j.1550-8528.1999.tb00417.x PubMedGoogle ScholarCrossref
    21.
    Elixhauser  A, Steiner  C, Palmer  L. Clinical Classifications Software (CCS) for ICD-9-CM. Databases and related tools from the Healthcare Cost and Utilization Project (HCUP): appendix a. 2012. Accessed August 30, 2021. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/AppendixASingleDX.txt
    22.
    Henderson  DC, Cagliero  E, Copeland  PM,  et al.  Elevated hemoglobin A1c as a possible indicator of diabetes mellitus and diabetic ketoacidosis in schizophrenia patients receiving atypical antipsychotics.   J Clin Psychiatry. 2007;68(4):533-541. doi:10.4088/JCP.v68n0407 PubMedGoogle ScholarCrossref
    23.
    Wetterhall  SF, Olson  DR, DeStefano  F,  et al.  Trends in diabetes and diabetic complications, 1980-1987.   Diabetes Care. 1992;15(8):960-967. doi:10.2337/diacare.15.8.960 PubMedGoogle ScholarCrossref
    24.
    Christakis  DA, Feudtner  C, Pihoker  C, Connell  FA.  Continuity and quality of care for children with diabetes who are covered by medicaid.   Ambul Pediatr. 2001;1(2):99-103. doi:10.1367/1539-4409(2001)001<0099:CAQOCF>2.0.CO;2 PubMedGoogle ScholarCrossref
    25.
    Khokhar  B, Jette  N, Metcalfe  A,  et al.  Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations.   BMJ Open. 2016;6(8):e009952. doi:10.1136/bmjopen-2015-009952 PubMedGoogle Scholar
    26.
    Saydah  SH, Geiss  LS, Tierney  E, Benjamin  SM, Engelgau  M, Brancati  F.  Review of the performance of methods to identify diabetes cases among vital statistics, administrative, and survey data.   Ann Epidemiol. 2004;14(7):507-516. doi:10.1016/j.annepidem.2003.09.016 PubMedGoogle ScholarCrossref
    27.
    Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.   J Clin Epidemiol. 1992;45(6):613-619. doi:10.1016/0895-4356(92)90133-8 PubMedGoogle ScholarCrossref
    28.
    D’Agostino  RB  Sr, Vasan  RS, Pencina  MJ,  et al.  General cardiovascular risk profile for use in primary care: the Framingham Heart Study.   Circulation. 2008;117(6):743-753. doi:10.1161/CIRCULATIONAHA.107.699579 PubMedGoogle ScholarCrossref
    29.
    Quan  H, Li  B, Saunders  LD,  et al; IMECCHI Investigators.  Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database.   Health Serv Res. 2008;43(4):1424-1441. doi:10.1111/j.1475-6773.2007.00822.x PubMedGoogle ScholarCrossref
    30.
    Goff  SL, Pekow  PS, Markenson  G, Knee  A, Chasan-Taber  L, Lindenauer  PK.  Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities.   Paediatr Perinat Epidemiol. 2012;26(5):421-429. doi:10.1111/j.1365-3016.2012.01303.x PubMedGoogle ScholarCrossref
    31.
    Stevens  LA, Coresh  J, Feldman  HI,  et al.  Evaluation of the modification of diet in renal disease study equation in a large diverse population.   J Am Soc Nephrol. 2007;18(10):2749-2757. doi:10.1681/ASN.2007020199 PubMedGoogle ScholarCrossref
    32.
    Becker  SO, Ichino  A.  Estimation of average treatment effects based on propensity scores.   Stata J. 2002;2(4):358-377. doi:10.1177/1536867X0200200403 Google ScholarCrossref
    33.
    Leuven  E, Sianesi  B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing, v 4.0.5. 2003. Accessed August 27, 2021. https://ideas.repec.org/c/boc/bocode/s432001.html
    34.
    Bender  R, Blettner  M.  Calculating the “number needed to be exposed” with adjustment for confounding variables in epidemiological studies.   J Clin Epidemiol. 2002;55(5):525-530. doi:10.1016/S0895-4356(01)00510-8 PubMedGoogle ScholarCrossref
    35.
    Mansi  IA.  Statins in primary prevention: uncertainties and gaps in randomized trial data.   Am J Cardiovasc Drugs. 2016;16(6):407-418. doi:10.1007/s40256-016-0190-3PubMedGoogle ScholarCrossref
    36.
    US Centers for Disease Control and Prevention. US diabetes surveillance system and diabetes atlas. Assessed December 9, 2020. https://www.cdc.gov/diabetes/data
    37.
    Gregg  EW, Hora  I, Benoit  SR.  Resurgence in diabetes-related complications.   JAMA. 2019;321(19):1867-1868. doi:10.1001/jama.2019.3471 PubMedGoogle ScholarCrossref
    38.
    Rawshani  A, Rawshani  A, Franzén  S,  et al.  Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes.   N Engl J Med. 2018;379(7):633-644. doi:10.1056/NEJMoa1800256 PubMedGoogle ScholarCrossref
    39.
    Rana  JS, Liu  JY, Moffet  HH, Jaffe  M, Karter  AJ.  Diabetes and prior coronary heart disease are not necessarily risk equivalent for future coronary heart disease events.   J Gen Intern Med. 2016;31(4):387-393. doi:10.1007/s11606-015-3556-3 PubMedGoogle ScholarCrossref
    40.
    Hu  FB, Stampfer  MJ, Solomon  CG,  et al.  The impact of diabetes mellitus on mortality from all causes and coronary heart disease in women: 20 years of follow-up.   Arch Intern Med. 2001;161(14):1717-1723. doi:10.1001/archinte.161.14.1717 PubMedGoogle ScholarCrossref
    41.
    Wannamethee  SG, Shaper  AG, Whincup  PH, Lennon  L, Sattar  N.  Impact of diabetes on cardiovascular disease risk and all-cause mortality in older men: influence of age at onset, diabetes duration, and established and novel risk factors.   Arch Intern Med. 2011;171(5):404-410. doi:10.1001/archinternmed.2011.2 PubMedGoogle ScholarCrossref
    42.
    Idris  I.  Diabetes and cardiovascular risk equivalency: do age at diagnosis and disease duration affect risk stratification?: comment on “impact of diabetes on cardiovascular disease risk and all-cause mortality in older men”.   Arch Intern Med. 2011;171(5):410-411. doi:10.1001/archinternmed.2010.524 PubMedGoogle ScholarCrossref
    43.
    Colhoun  HM, Betteridge  DJ, Durrington  PN,  et al; CARDS investigators.  Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial.   Lancet. 2004;364(9435):685-696. doi:10.1016/S0140-6736(04)16895-5 PubMedGoogle ScholarCrossref
    44.
    Swerdlow  DI, Preiss  D, Kuchenbaecker  KB,  et al; DIAGRAM Consortium; MAGIC Consortium; InterAct Consortium.  HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials.   Lancet. 2015;385(9965):351-361. doi:10.1016/S0140-6736(14)61183-1 PubMedGoogle ScholarCrossref
    45.
    Henriksbo  BD, Lau  TC, Cavallari  JF,  et al.  Fluvastatin causes NLRP3 inflammasome-mediated adipose insulin resistance.   Diabetes. 2014;63(11):3742-3747. doi:10.2337/db13-1398 PubMedGoogle ScholarCrossref
    46.
    Mitchell  P, Marette  A.  Statin-induced insulin resistance through inflammasome activation: sailing between Scylla and Charybdis.   Diabetes. 2014;63(11):3569-3571. doi:10.2337/db14-1059 PubMedGoogle ScholarCrossref
    47.
    Buse  MG.  Hexosamines, insulin resistance, and the complications of diabetes: current status.   Am J Physiol Endocrinol Metab. 2006;290(1):E1-E8. doi:10.1152/ajpendo.00329.2005 PubMedGoogle ScholarCrossref
    48.
    Kernan  WN, Viscoli  CM, Furie  KL,  et al; IRIS Trial Investigators.  Pioglitazone after ischemic stroke or transient ischemic attack.   N Engl J Med. 2016;374(14):1321-1331. doi:10.1056/NEJMoa1506930 PubMedGoogle ScholarCrossref
    49.
    Mather  KJ, Steinberg  HO, Baron  AD.  Insulin resistance in the vasculature.   J Clin Invest. 2013;123(3):1003-1004. doi:10.1172/JCI67166 PubMedGoogle ScholarCrossref
    50.
    Semenkovich  CF.  Insulin resistance and atherosclerosis.   J Clin Invest. 2006;116(7):1813-1822. doi:10.1172/JCI29024 PubMedGoogle ScholarCrossref
    51.
    Smith  AG, Singleton  JR.  Obesity and hyperlipidemia are risk factors for early diabetic neuropathy.   J Diabetes Complications. 2013;27(5):436-442. doi:10.1016/j.jdiacomp.2013.04.003 PubMedGoogle ScholarCrossref
    52.
    Kissel  JT, Smith  AG.  Understanding small fiber neuropathy: the long and short of it.   JAMA Neurol. 2016;73(6):635-637. doi:10.1001/jamaneurol.2016.0256 PubMedGoogle ScholarCrossref
    53.
    Verma  A, Visintainer  P, Elarabi  M, Wartak  S, Rothberg  MB.  Overtreatment and undertreatment of hyperlipidemia in the outpatient setting.   South Med J. 2012;105(7):329-333. doi:10.1097/SMJ.0b013e318259bad3 PubMedGoogle ScholarCrossref
    54.
    Mansi  IA, Frei  CR, Halm  EA, Mortensen  EM.  Association of statins with diabetes mellitus and diabetic complications: role of confounders during follow-up.   J Investig Med. 2017;65(1):32-42. doi:10.1136/jim-2016-000218 PubMedGoogle ScholarCrossref
    55.
    Raebel  MA, Schroeder  E, Goodrich  G,  et al Validating type 1 and type 2 diabetes mellitus in the Mini-Sentinel Distributed Database using the Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM) DataLink. Accessed on March 4, 2021. https://www.sentinelsystem.org/sentinel/methods/validating-type-1-and-type-2-diabetes-mellitus-mini-sentinel-distributed-database
    56.
    Wong  ES, Wang  V, Liu  CF, Hebert  PL, Maciejewski  ML.  Do Veterans Health Administration enrollees generalize to other populations?   Med Care Res Rev. 2016;73(4):493-507. doi:10.1177/1077558715617382 PubMedGoogle ScholarCrossref
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