Cited by 117Diabetes Treatment and ManagementBone health and osteoporosis researchPharmaceutical Practices and Patient Outcomes

Effects of anti-diabetic drugs on fracture risk: a systematic review and network meta-analysis

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Zhang, Yu-Sheng, Zheng, Yan-Dan, Yuan, Yan, Chen, Shi-Chun, Xie, Bao-Cheng·2021·Frontiers in Endocrinology

Abstract

Purpose: Available data on the effects of anti-diabetic drugs on fracture risk are contradictory. Therefore, our study aimed to analyze all available data on the effects of anti-diabetic drugs on fracture risk in type 2 diabetes mellitus (T2DM) patients. Methods: Embase, Medline, ClinicalTrials.gov, and Cochrane CENTRAL were searched for relevant trials. All data analyses were performed with STATA (12.0) and R language (3.6.0). Risk ratio (RR) with its 95% confidence interval (CI) was calculated by combining data for the fracture effects of anti-diabetic drugs, including sodium–glucose co-transporter 2 (SGLT2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, meglitinides, α-glucosidase inhibitors, thiazolidinediones, biguanides, insulin, and sulfonylureas. Results: One hundred seventeen eligible randomized controlled trials (RCTs) with 221,364 participants were included in this study. Compared with placebo, trelagliptin (RR 3.51; 1.58–13.70) increased the risk of fracture, whereas albiglutide (RR 0.29; 0.04–0.93) and voglibose (RR 0.03; 0–0.11) decreased the risk of fracture. Other medications were comparable in terms of their effects on fracture risk, and no statistical significance was observed. In terms of fractures, voglibose (0.01%) may be the safest option, and trelagliptin (13.64%) may be the worst. Sensitivity analysis results were consistent with those of the main analysis. No statistically significant differences were observed in the regression coefficients of age (1.03; 0.32–2.1), follow-up duration (0.79; 0.27–1.64), and sex distribution (0.63; 0.15–1.56). Conclusions: We found varied results on the association between the use of anti-diabetic drugs and fracture risk. Specifically, trelagliptin raised the risk of fracture, whereas voglibose and albiglutide showed benefit with statistical difference. Other drugs were comparable in terms of their effects on fracture risk. Some drugs (omarigliptin, sitagliptin, vildagliptin, saxagliptin, empagliflozin, ertugliflozin, rosiglitazone, pioglitazone, and nateglinide) may increase the risk of fracture, while others (such as dulaglutide, exenatide, liraglutide, semaglutide, lixisenatide, linagliptin, alogliptin, canagliflozin, dapagliflozin, glipizide, gliclazide, glibenclamide, glimepiride, metformin, and insulin) may show benefits. The risk of fracture was independent of age, sex distribution, and the duration of exposure to anti-diabetic drugs. When developing individualized treatment strategies, the clinical efficacy of anti-diabetic drugs must be weighed against their benefits and risks brought about by individual differences of patients. Systematic Review Registration: This Systematic Review was prospectively registered on the PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number CRD42020189464).

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  • Meglitinides (repaglinide and nateglinide) increase the risk of hypoglycemia and fracture risk.

    “meglitinides (repaglinide and nateglinide) increase the risk of hypoglycemia and fracture risk”

    From 13. Older Adults: Standards of Care in Diabetes—2026 (2026) · cites this paper as [123]

    The Results (‘Others’; page unavailable) report a higher fracture-risk estimate for nateglinide versus placebo (RR 1.35), but its confidence interval (0.24–7.55) includes no increase; the Conclusions accordingly say it may increase risk. The copy does not establish increased fracture risk for repaglinide or increased hypoglycemia risk for either meglitinide. The Discussion’s reference to hypoglycemic episodes concerns sulfonylureas, not meglitinides. (LLM, conf=0.88)

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References 46 · Cited by 117

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  1. [1]Assar ME Laosa O Rodríguez ML. Diabetes and Frailty. Curr Opin Clin Nutr Metab Care (2019) 22(1): 52–7. doi: 10.1097/MCO.0000000000000535 30394893 · DOI
  2. [3]Compston J. Type 2 Diabetes Mellitus and Bone. J Intern Med (2018) 283(2): 140–53. doi: 10.1111/joim.12725 29265670 · DOI
  3. [4]Kurra S Fink DA Siris ES. Osteoporosis-Associated Fracture and Diabetes. Endocrinol Metab Clin North Am (2014) 43(1): 233–43. doi: 10.1016/j.ecl.2013.09.004 24582100 · DOI
  4. [5]Eller-Vainicher C Cairoli E Grassi G Grassi F Catalano A Merlotti D. Pathophysiology and Management of Type 2 Diabetes Mellitus Bone Fragility. J Diabetes Res (2020) 2020: 1– 18. doi: 10.1155/2020/7608964 PMC7262667 32566682 · DOI
  5. [6]Jiang N Xia W. Assessment of Bone Quality in Patients With Diabetes Mellitus. Osteoporos Int (2018) 29(8): 1721–36. doi: 10.1007/s00198-018-4532-7 29736760 · DOI
  6. [7]Jiao H Xiao E Graves DT. Diabetes and Its Effect on Bone and Fracture Healing. Curr Osteoporos Rep (2015) 13(5): 327–35. doi: 10.1007/s11914-015-0286-8 PMC4692363 26254939 · DOI
  7. [8]Kalaitzoglou E Fowlkes JL Popescu I Thrailkill KM. Diabetes Pharmacotherapy and Effects on the Musculoskeletal System. Diabetes Metab Res Rev (2019) 35(2): e3100. doi: 10.1002/dmrr.3100 30467957 PMC6358500 · DOI
  8. [9]Singla R Gupta Y Kalra S. Musculoskeletal Effects of Diabetes Mellitus. J Pak Med Assoc (2015) 65(9): 1024–7. 26338757
  9. [10]Cortet B Lucas S Legroux-Gerot I Penel G Chauveau C Paccou J. Bone Disorders Associated With Diabetes Mellitus and its Treatments. Joint Bone Spine (2019) 86(3): 315–20. doi: 10.1016/j.jbspin.2018.08.002 30098423 · DOI
  10. [12]Jackuliak P Kužma M Payer J. Effect of Antidiabetic Treatment on Bone. Physiol Res (2019) 68(Suppl 2): S107–20. doi: 10.33549/physiolres.934297 31842574 · DOI
  11. [13]Hygum K Starup-Linde J Langdahl BL. Diabetes and Bone. Osteoporos Sarcopenia (2019) 5(2): 29– 37. doi: 10.1016/j.afos.2019.05.001 31346556 PMC6630041 · DOI
  12. [14]Romero-Díaz C Duarte-Montero D Gutiérrez-Romero SA Mendivil CO. Diabetes and Bone Fragility. Diabetes Ther (2021) 12(1): 71– 86. doi: 10.1007/s13300-020-00964-1 33185853 PMC7843783 · DOI
  13. [15]Faltinsen EG Storebø OJ Jakobsen JC Boesen K Lange T Gluud C. Network Meta-Analysis: The Highest Level of Medical Evidence? BMJ Evid Based Med (2018) 23(2): 56–9. doi: 10.1136/bmjebm-2017-110887 29595131 · DOI
  14. [16]Higgins JP Altman DG Gøtzsche PC Jüni P Moher D Oxman AD. The Cochrane Collaboration’s Tool for Assessing Risk of Bias in Randomised Trials. BMJ (2011) 343: d5928. doi: 10.1136/bmj.d5928 22008217 PMC3196245 · DOI · Paper page
  15. [17]Endo A van Leeuwen E Baguelin M. Introduction to Particle Markov-Chain Monte Carlo for Disease Dynamics Modellers. Epidemics-Neth (2019) 29: 100363. doi: 10.1016/j.epidem.2019.100363 31587877 · DOI
  16. [19]Moreno E Vázquez-Polo FJ Negrín MA. Bayesian Meta-Analysis: The Role of the Between-Sample Heterogeneity. Stat Methods Med Res (2018) 27(12): 3643–57. doi: 10.1177/0962280217709837 28511616 · DOI
  17. [20]Dhamala J Arevalo HJ Sapp J Horácek BM Wu KC Trayanova NA. Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo in Cardiac Electrophysiology. Med Image Anal (2018) 48: 43– 57. doi: 10.1016/j.media.2018.05.007 29843078 PMC6076346 · DOI
  18. [21]Moran JL Graham PL. Risk Related Therapy in Meta-Analyses of Critical Care Interventions: Bayesian Meta-Regression Analysis. J Crit Care (2019) 53: 114–9. doi: 10.1016/j.jcrc.2019.06.003 31228761 · DOI
  19. [22]Vázquez-Polo FJ Moreno E Negrín MA Martel M. A Bayesian Sensitivity Study of Risk Difference in the Meta-Analysis of Binary Outcomes From Sparse Data. Expert Rev Pharmacoecon Outcomes Res (2015) 15(2): 317–22. doi: 10.1586/14737167.2015.1011131 25673174 · DOI
  20. [23]Dias S Sutton AJ Welton NJ Ades AE. Evidence Synthesis for Decision Making 3: Heterogeneity–Subgroups, Meta-Regression, Bias, and Bias-Adjustment. Med Decis Making (2013) 33(5): 618–40. doi: 10.1177/0272989X13485157 PMC3704206 23804507 · DOI
  21. [24]Stroup DF Berlin JA Morton SC Olkin I Williamson GD Rennie D. Meta-Analysis of Observational Studies in Epidemiology: A Proposal for Reporting. Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) Group. JAMA (2000) 283(15): 2008–12. doi: 10.1001/jama.283.15.2008 10789670 · DOI · Paper page
  22. [25]Zeng I Lumley T. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights (2018) 12: 1177932218759292. doi: 10.1177/1177932218759292 29497285 PMC5824897 · DOI
  23. [26]Zhao C Liang J Yang Y Yu M Qu X. The Impact of Glucagon-Like Peptide-1 on Bone Metabolism and Its Possible Mechanisms. Front Endocrinol (2017) 8: 98. doi: 10.3389/fendo.2017.00098 PMC5413504 28515711 · DOI
  24. [27]Hidayat K Du X Shi BM. Risk of Fracture With Dipeptidyl Peptidase-4 Inhibitors, Glucagon-Like Peptide-1 Receptor Agonists, or Sodium-Glucose Cotransporter-2 Inhibitors in Real-World Use: Systematic Review and Meta-Analysis of Observational Studies. Osteoporosis Int (2019) 30(10): 1923–40. doi: 10.1007/s00198-019-04968-x 31134305 · DOI
  25. [29]Mannucci E Dicembrini I. Drugs for Type 2 Diabetes: Role in the Regulation of Bone Metabolism. Clin cases Mineral Bone Metab (2015) 12(2): 130–4. doi: 10.11138/ccmbm/2015.12.2.130 PMC4625768 26604937 · DOI
  26. [31]Fu J Zhu J Hao Y Guo C Zhou Z. Dipeptidyl Peptidase-4 Inhibitors and Fracture Risk: An Updated Meta-Analysis of Randomized Clinical Trials. Sci Rep-UK (2016) 6(1): 29104. doi: 10.1038/srep29104 PMC4935882 27384445 · DOI
  27. [32]Driessen JHM van den Bergh JPW van Onzenoort HAW Henry RMA Leufkens HGM de Vries F. Long-Term Use of Dipeptidyl Peptidase-4 Inhibitors and Risk of Fracture: A Retrospective Population-Based Cohort Study. Diabetes Obes Metab (2017) 19(3): 421–8. doi: 10.1111/dom.12843 27943565 · DOI
  28. [33]Wolverton D Blair MM. Fracture Risk Associated With Common Medications Used in Treating Type 2 Diabetes Mellitus. Am J Health-Syst Ph (2017) 74(15): 1143–51. doi: 10.2146/ajhp160319 28743778 · DOI
  29. [34]Lou Y Yu Y Duan J Bi S Swe K Xi Z. Sodium-Glucose Cotransporter 2 Inhibitors and Fracture Risk in Patients With Type 2 Diabetes Mellitus: A Meta-Analysis of Randomized Controlled Trials. Ther Adv Chronic Dis (2020) 11: 2040622320961599. doi: 10.1177/2040622320961599 33062238 PMC7534105 · DOI
  30. [36]Hidayat K Du X Wu MJ Shi BM. The Use of Metformin, Insulin, Sulphonylureas, and Thiazolidinediones and the Risk of Fracture: Systematic Review and Meta-Analysis of Observational Studies. Obes Rev (2019) 20(10): 1494–503. doi: 10.1111/obr.12885 31250977 · DOI
  31. [37]Murphy CE Rodgers PT. Effects of Thiazolidinediones on Bone Loss and Fracture. Ann Pharmacother (2007) 41(12): 2014–8. doi: 10.1345/aph.1K286 17940125 · DOI
  32. [38]Pavlova V Filipova E Uzunova K Kalinov K Vekov T. Pioglitazone Therapy and Fractures: Systematic Review and Meta- Analysis. Endocr Metab Immune Disord Drug Targets (2018) 18(5): 502. doi: 10.2174/1871530318666180423121833 29683100 · DOI
  33. [39]Tao Y E M Shi J Zhang Z. Sulfonylureas Use and Fractures Risk in Elderly Patients With Type 2 Diabetes Mellitus: A Meta-Analysis Study. Aging Clin Exp Res (2020) 33(8): 2133–39. doi: 10.1007/s40520-020-01736-4 33104983 · DOI
  34. [40]Paschou SA Dede AD Anagnostis PG Vryonidou A Morganstein D Goulis DG. Goulis: Type 2 Diabetes and Osteoporosis: A Guide to Optimal Management. J Clin Endocrinol Metab (2017) 102(10): 3621–34. doi: 10.1210/jc.2017-00042 28938433 · DOI
  35. [41]Adil M Khan RA Kalam A Venkata SK Kandhare AD Ghosh P. Effect of Anti-Diabetic Drugs on Bone Metabolism: Evidence From Preclinical and Clinical Studies. Pharmacol Rep (2017) 69(6): 1328–40. doi: 10.1016/j.pharep.2017.05.008 29132091 · DOI
  36. [42]Salari-Moghaddam A Sadeghi O Keshteli AH Larijani B Esmaillzadeh A. Metformin Use and Risk of Fracture: A Systematic Review and Meta-Analysis of Observational Studies. Osteoporosis Int (2019) 30(6): 1167–73. doi: 10.1007/s00198-019-04948-1 30927035 · DOI
  37. [43]Al-Mashhadi Z Viggers R Fuglsang-Nielsen R de Vries F van den Bergh JP Harsløf T. Glucose-Lowering Drugs and Fracture Risk—A Systematic Review. Curr Osteoporosis Rep (2020) 18(6): 737–58. doi: 10.1007/s11914-020-00638-8 33165875 · DOI
  38. [44]Günhan BK Friede T Held L. A Design-by-Treatment Interaction Model for Network Meta-Analysis and Meta-Regression With Integrated Nested Laplace Approximations. Res Synth Methods (2018) 9(2): 179–94. doi: 10.1002/jrsm.1285 PMC6001639 29193801 · DOI

Authors
Zhang, Y.-S., Zheng, Y.-D., Yuan, Y., Chen, S.-C., & Xie, B.-C.
Affiliations
First People's Hospital of Foshan (CN)
Venue
Frontiers in Endocrinology
Published
2021-10-14
Volume
12
Pages
735824-735824
ISSN
1664-2392 (ISSN-L)
OpenAlex
W3206235521
Cited by
117
References
46
Citations by year
2026: 18 · 2025: 29 · 2024: 28 · 2023: 21 · 2022: 20 · 2021: 1
FWCI
2.69
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (82000842)
  • Natural Science Foundation of Guangdong Province (2021A1515010151)
  • Traditional Chinese Medicine Bureau of Guangdong Province (20211403)
  • Guangdong Medical Research Foundation (B2020086)
  • Dongguan Science and Technology Bureau (202050715001197)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
Keywords
antidiabetic drugs, fracture risk, type 2 diabetes, SGLT2 inhibitors, DPP-4 inhibitors, GLP-1 receptor agonists, meglitinides, alpha-glucosidase inhibitors, thiazolidinediones, biguanides, insulin, sulfonylureas, trelagliptin, albiglutide, voglibose
MeSH terms
Network Meta-Analysis, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Risk Factors, Fractures, Bone

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