Sodium-glucose co-transporter 2 inhibitors and the risk of fractures: A propensity score-matched cohort study.
Adult
Aged
Diabetes Mellitus, Type 2
/ drug therapy
Dipeptidyl-Peptidase IV Inhibitors
/ adverse effects
Female
Follow-Up Studies
Fractures, Bone
/ chemically induced
Humans
Incidence
Male
Middle Aged
Propensity Score
Retrospective Studies
Risk Factors
Sodium-Glucose Transporter 2 Inhibitors
/ adverse effects
SGLT2 inhibitors
cohort
fractures
safety
study diabetes
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
14
02
2019
revised:
27
08
2019
accepted:
01
09
2019
pubmed:
28
10
2019
medline:
28
7
2020
entrez:
25
10
2019
Statut:
ppublish
Résumé
To determine the risk of fractures associated with sodium-glucose co-transporter 2 inhibitors (SGLT2i) compared with dipeptidyl peptidase-4 inhibitors (DPP4i). We conducted a retrospective cohort study using data from the Truven Health MarketScan (2009-2015) databases. Our cohort included patients newly initiating treatment with SGLT2i or DPP-4i between 1 April 2013 and 31 March 2015 that were matched 1:1 using high dimensional propensity scores. Patients were followed up in an as-treated approach starting from initiation of treatment until the earliest of any fracture, treatment discontinuation, disenrollment, or end of data (31 December 2015). Risk of fractures was determined at any time during the follow-up, early in therapy (1-14 days of the follow-up), and later in therapy (15 days and beyond). Cox proportional hazards models were used to determine hazard ratios and robust 95% confidence intervals (95% CI). After matching, our cohort included 30 549 patients in each treatment group. Over a median follow-up of 219 days, there were 745 fractures overall. The most common site for fractures was the foot (32.7%). The effect estimates for fracture risk occurring at any time during follow-up, early in therapy, and later in therapy were HR 1.11 [95% CI 0.96-1.28], HR 1.82 [95% CI 0.99-3.32], and HR 1.07 [95% CI 0.92-1.24], respectively. There is a possible increase in risk for fractures early in therapy with SGLT2i. Beyond this initial period, SGLT2is had no apparent effect on the incidence of fractures.
Substances chimiques
Dipeptidyl-Peptidase IV Inhibitors
0
Sodium-Glucose Transporter 2 Inhibitors
0
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1629-1639Informations de copyright
© 2019 John Wiley & Sons, Ltd.
Références
US Food and Drug Administration. Safety alerts for human medical products-Invokana and Invokamet (canagliflozin): Drug safety communication-New information on bone fracture risk and decreased bone mineral density. http://www.fda.gov/Safety/MedWatch/SafetyInformation/SafetyAlertsforHumanMedicalProducts/ucm461876.htm.
Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644-657. https://doi.org/10.1056/NEJMoa1611925
Watts NB, Bilezikian JP, Usiskin K, et al. Effects of canagliflozin on fracture risk in patients with type 2 diabetes mellitus. J Clin Endocrinol Metab. 2016;101(1):157-166. https://doi.org/10.1210/jc.2015-3167
Ruanpeng D, Ungprasert P, Sangtian J, Harindhanavudhi T. Sodium-glucose cotransporter 2 (SGLT2) inhibitors and fracture risk in patients with type 2 diabetes mellitus: A meta-analysis. Diabetes Metab Res Rev. 2017;33(6):e2903. https://doi.org/10.1002/dmrr.2903
Tang HL, Li DD, Zhang JJ, et al. Lack of evidence for a harmful effect of sodium-glucose co-transporter 2 (SGLT2) inhibitors on fracture risk among type 2 diabetes patients: A network and cumulative meta-analysis of randomized controlled trials. Diabetes Obes Metab. 2016;18(12):1199-1206. https://doi.org/10.1111/dom.12742
Kohler S, Kaspers S, Salsali A, Zeller C, Woerle HJ. Analysis of fractures in patients with type 2 diabetes treated with empagliflozin in pooled data from placebo-controlled trials and a head-to-head study versus glimepiride. Diabetes Care. June 2018:dc171525. https://doi.org/10.2337/dc17-1525, 41, 8, 1809, 1816
Schwartz AV. Diabetes, bone and glucose-lowering agents: Clinical outcomes. Diabetologia. April 2017;60(7):1-10. https://doi.org/10.1007/s00125-017-4283-6
Mannucci E, Monami M. Bone fractures with sodium-glucose co-transporter-2 inhibitors: How real is the risk? Drug Saf. 2017;40(2):115-119. https://doi.org/10.1007/s40264-016-0470-5
Alba M, Xie J, Fung A, Desai M. The effects of canagliflozin, a sodium glucose co-transporter 2 inhibitor, on mineral metabolism and bone in patients with type 2 diabetes mellitus. Curr Med Res Opin. 2016;32(8):1375-1385. https://doi.org/10.1080/03007995.2016.1174841
Blau JE, Bauman V, Conway EM, et al. Canagliflozin triggers the FGF23/1,25-dihydroxyvitamin D/PTH axis in healthy volunteers in a randomized crossover study. JCI Insight. 2018;3(8):e99123. https://doi.org/10.1172/jci.insight.99123
Hansen LG, Chang S. Health research data for the real world: The MarketScan databases. [White Paper]. Truven Health: 2011. truvenhealth.com/portals/0/assets/PH_11238_0612_TEMP_MarketScan_WP_FINAL.pdf.
Suissa S, Azoulay L. Metformin and the risk of cancer. Diabetes Care. 2012;35(12):2665-2673. https://doi.org/10.2337/dc12-0788
Adimadhyam S, Lee TA, Calip GS, Marsh DES, Layden BT, Schumock GT. Risk of amputations associated with SGLT2 inhibitors compared to DPP-4 inhibitors: A propensity-matched cohort study. Diabetes Obes Metab. 2018;20(12):1-8. https://doi.org/10.1111/dom.13459
Suissa S. Lower Risk of Death With SGLT2 Inhibitors in observational studies: Real or bias? Diabetes Care. 2018;41(1):6-10. https://doi.org/10.2337/dc17-1223
Reusch JEB, Manson JE. Management of type 2 diabetes in 2017: Getting to goal. JAMA. 2017;317(10):1015-1016. https://doi.org/10.1001/jama.2017.0241
Suissa S. Response to Comment on Suissa. Lower risk of death with SGLT2 inhibitors in observational studies: Real or bias? Diabetes Care 2018;41:6-10. Diabetes Care. 2018;41(6):e109-e110. https://doi.org/10.2337/dci18-0015
Ray WA, Griffin MR, Fought RL, Adams ML. Identification of fractures from computerized medicare files. J Clin Epidemiol. 1992;45(7):703-714. https://doi.org/10.1016/0895-4356(92)90047-Q
Chang HY, Weiner JP, Richards TM, Bleich SN, Segal JB. Validating the adapted diabetes complications severity index in claims data. Am J Manag Care. 2012;18(11):721-726.
Munson JC, Bynum JPW, Bell J-E, et al. Patterns of prescription drug use before and after fragility fracture. JAMA Intern Med. 2016;176(10):1531-1538. https://doi.org/10.1001/jamainternmed.2016.4814
Golden AG, Ma Q, Nair V, Florez HJ, Roos BA. Risk for fractures with centrally acting muscle relaxants: An analysis of a national Medicare Advantage claims database. Ann Pharmacother. 2010;44(9):1369-1375. https://doi.org/10.1345/aph.1P210
Rosen HN. Drugs that affect bone metabolism. In: Mulder JE, ed. UpToDate. Waltham, MA: UpToDate Inc. https://www.uptodate.com. .
Lindsay R, Cosman F. Osteoporosis. In: Kasper D, Fauci A, Hauser S, Longo D, Jameson JL, Loscalzo J, eds. Harrison's Principles of Internal Medicine, 19e. New York, NY: McGraw-Hill Education; 2018. accesspharmacy.mhmedical.com/content.aspx?aid=1120817090.
Faurot KR, Jonsson Funk M, Pate V, et al. Using claims data to predict dependency in activities of daily living as a proxy for frailty. Pharmacoepidemiol Drug Saf. 2015;24(1):59-66. https://doi.org/10.1002/pds.3719
Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol. 2011;64(7):749-759. https://doi.org/10.1016/j.jclinepi.2010.10.004
Desai RJ, Solomon DH, Shadick N, Iannaccone C, Kim SC. Identification of smoking using Medicare data-A validation study of claims-based algorithms. Pharmacoepidemiol Drug Saf. 2016;25(4):472-475. https://doi.org/10.1002/pds.3953
Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512-522.
Rassen JA, Doherty M, Huang W, Schneeweiss S. Pharmacoepidemiology Toolbox. Boston, MA. www.hdpharmacoepi.org.
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150-161. https://doi.org/10.1002/pst.433
Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat - Simul Comput. 2009;38(6):1228-1234. https://doi.org/10.1080/03610910902859574
Nguyen T-L, Collins GS, Spence J, et al. Double-adjustment in propensity score matching analysis: Choosing a threshold for considering residual imbalance. BMC Med Res Methodol. 2017;17(1):78. https://doi.org/10.1186/s12874-017-0338-0
Oliva RV, Bakris GL. Blood pressure effects of sodium-glucose co-transport 2 (SGLT2) inhibitors. J Am Soc Hypertens. 2014;8(5):330-339. https://doi.org/10.1016/j.jash.2014.02.003
Butt DA, Mamdani M, Austin PC, Tu K, Gomes T, Glazier RH. The risk of falls on initiation of antihypertensive drugs in the elderly. Osteoporos Int. 2013;24(10):2649-2657. https://doi.org/10.1007/s00198-013-2369-7
Gribbin J, Hubbard R, Gladman JRF, Smith C, Lewis S. Risk of falls associated with antihypertensive medication: Population-based case-control study. Age Ageing. 2010;39(5):592-597. https://doi.org/10.1093/ageing/afq092
Wagner AK, Zhang F, Soumerai SB, et al. Benzodiazepine use and hip fractures in the elderly: Who is at greatest risk? Arch Intern Med. 2004;164(14):1567-1572. https://doi.org/10.1001/archinte.164.14.1567
Austin PC. The use of propensity score methods with survival or time-to-event outcomes: Reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242-1258. https://doi.org/10.1002/sim.5984
Wang SV, Jin Y, Fireman B, et al. Relative performance of propensity score matching strategies for subgroup analyses. Am J Epidemiol. 2018;187(8):1799-1807. https://doi.org/10.1093/aje/kwy049
Rassen JA, Glynn RJ, Rothman KJ, Setoguchi S, Schneeweiss S. Applying propensity scores estimated in a full cohort to adjust for confounding in subgroup analyses. Pharmacoepidemiol Drug Saf. 2012;21(7):697-709. https://doi.org/10.1002/pds.2256
Bonds DE, Larson JC, Schwartz AV, et al. Risk of fracture in women with type 2 diabetes: the Women's Health Initiative Observational Study. J Clin Endocrinol Metab. 2006;91(9):3404-3410. https://doi.org/10.1210/jc.2006-0614
Scheen A. Pharmacodynamics, Efficacy and safety of sodium-glucose co-transporter type 2 (SGLT2) inhibitors for the treatment of type 2 diabetes mellitus. Drugs. 2015;75(1):33-59. https://doi.org/10.1007/s40265-014-0337-y
Toulis KA, Bilezikian JP, Thomas GN, et al. Initiation of dapagliflozin and treatment-emergent fractures. Diabetes Obes Metab. 2018;20(4):1070-1074. https://doi.org/10.1111/dom.13176
Loke YK, Singh S, Furberg CD. Long-term use of thiazolidinediones and fractures in type 2 diabetes: A meta-analysis. CMAJ Can Med Assoc J. 2009;180(1):32-39. https://doi.org/10.1503/cmaj.080486
Bazelier MT, Gallagher AM, van Staa T-P, et al. Use of thiazolidinediones and risk of osteoporotic fracture: Disease or drugs? Pharmacoepidemiol Drug Saf. 2012;21(5):507-514. https://doi.org/10.1002/pds.3234
Meier C, Kraenzlin ME, Bodmer M, Jick SS, Jick H, Meier CR. Use of thiazolidinediones and fracture risk. Arch Intern Med. 2008;168(8):820-825. https://doi.org/10.1001/archinte.168.8.820
Schmedt N, Andersohn F, Walker J, Garbe E. Sodium-glucose co-transporter-2 inhibitors and the risk of fractures of the upper or lower limbs in patients with type 2 diabetes: A nested case-control study. Diabetes Obes Metab. 2019;21(1):52-60. https://doi.org/10.1111/dom.13480
Ueda P, Svanström H, Melbye M, et al. Sodium glucose cotransporter 2 inhibitors and risk of serious adverse events: nationwide register based cohort study. BMJ. 2018;363:k4365. https://doi.org/10.1136/bmj.k4365
Fralick M, Kim SC, Schneeweiss S, Kim D, Redelmeier DA, Patorno E. Fracture risk after initiation of use of canagliflozin: A cohort study. Ann Intern Med. January 2019;170(3):155. https://doi.org/10.7326/M18-0567
Lu CY, Stewart C, Ahmed AT, et al. How complete are E-codes in commercial plan claims databases? Pharmacoepidemiol Drug Saf. 2014;23(2):218-220. https://doi.org/10.1002/pds.3551
Suissa S, Moodie EEM, Dell'Aniello S. Prevalent new-user cohort designs for comparative drug effect studies by time-conditional propensity scores. Pharmacoepidemiol Drug Saf. 2017;26(4):459-468. https://doi.org/10.1002/pds.4107
Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70-75. https://doi.org/10.1016/j.jclinepi.2016.04.014