SGLT2 Inhibitor Use and Risk of Breast Cancer Among Adult Women with Type 2 Diabetes

Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660.

Article  PubMed  Google Scholar 

Economopoulou P, Dimitriadis G, Psyrri A. Beyond BRCA: new hereditary breast cancer susceptibility genes. Cancer Treat Rev. 2015;41:1–8. https://doi.org/10.1016/j.ctrv.2014.10.008.

Article  CAS  PubMed  Google Scholar 

Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022;18:525–39. https://doi.org/10.1038/s41574-022-00690-7.

Article  PubMed  PubMed Central  Google Scholar 

Khan MAB, Hashim MJ, King JK, et al. Epidemiology of type 2 diabetes—global burden of disease and forecasted trends. J Epidemiol Glob Health. 2020;10:107–11. https://doi.org/10.2991/jegh.k.191028.001.

Article  PubMed  PubMed Central  Google Scholar 

Tang H, Dai Q, Shi W, et al. SGLT2 inhibitors and risk of cancer in type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials. Diabetologia. 2017;60:1862–72. https://doi.org/10.1007/s00125-017-4370-8.

Article  CAS  PubMed  Google Scholar 

Boyle P, Boniol M, Koechlin A, et al. Diabetes and breast cancer risk: a meta-analysis. Br J Cancer. 2012;107:1608–17. https://doi.org/10.1038/bjc.2012.414.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927;8:519–30.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lupsa BC, Inzucchi SE. Use of SGLT2 inhibitors in type 2 diabetes: weighing the risks and benefits. Diabetologia. 2018;61:2118–25. https://doi.org/10.1007/s00125-018-4663-6.

Article  CAS  PubMed  Google Scholar 

Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes—2022 | Diabetes Care | American Diabetes Association. https://diabetesjournals.org/care/article/45/Supplement_1/S125/138908/9-Pharmacologic-Approaches-to-Glycemic-Treatment. Accessed 24 Jan 2023

Fonseca-Correa JI, Correa-Rotter R. Sodium-glucose cotransporter 2 inhibitors mechanisms of action: a review. Front Med. 2021;8: 777861. https://doi.org/10.3389/fmed.2021.777861.

Article  Google Scholar 

Lambers Heerspink HJ, de Zeeuw D, Wie L, et al. Dapagliflozin a glucose-regulating drug with diuretic properties in subjects with type 2 diabetes. Diabetes Obes Metab. 2013;15:853–62. https://doi.org/10.1111/dom.12127.

Article  CAS  PubMed  Google Scholar 

Lee T-M, Chang N-C, Lin S-Z. Dapagliflozin, a selective SGLT2 Inhibitor, attenuated cardiac fibrosis by regulating the macrophage polarization via STAT3 signaling in infarcted rat hearts. Free Radic Biol Med. 2017;104:298–310. https://doi.org/10.1016/j.freeradbiomed.2017.01.035.

Article  CAS  PubMed  Google Scholar 

Komatsu S, Nomiyama T, Numata T, et al. SGLT2 inhibitor ipragliflozin attenuates breast cancer cell proliferation. Endocrine J. 2020;67:99–106. https://doi.org/10.1507/endocrj.EJ19-0428.

Article  CAS  Google Scholar 

Komatsu S, Nomiyama T, Numata T, et al. SGLT2 Inhibitor ipragliflozin induces breast cancer apoptosis via membrane hyperpolarization and mitochondria dysfunction. Diabetes. 2018;67:255-OR. https://doi.org/10.2337/db18-255-OR.

Article  Google Scholar 

Ware K, Smith T, Brown D-V, et al. The effect of sodium glucose transporter 2 inhibitors on proliferation and growth factor signaling pathways in triple negative breast cancer. FASEB J. 2019;33:647.48. https://doi.org/10.1096/fasebj.2019.33.1_supplement.647.48.

Article  Google Scholar 

Nasiri AR, Rodrigues MR, Li Z, et al. SGLT2 inhibition slows tumor growth in mice by reversing hyperinsulinemia. Cancer Metab. 2019;7:10. https://doi.org/10.1186/s40170-019-0203-1.

Article  PubMed  PubMed Central  Google Scholar 

Zhou J, Zhu J, Yu S-J, et al. Sodium-glucose co-transporter-2 (SGLT-2) inhibition reduces glucose uptake to induce breast cancer cell growth arrest through AMPK/mTOR pathway. Biomed Pharmacother. 2020;132: 110821. https://doi.org/10.1016/j.biopha.2020.110821.

Article  CAS  PubMed  Google Scholar 

Benedetti R, Benincasa G, Glass K, et al. Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials. Pharmacol Res. 2022;175: 106039. https://doi.org/10.1016/j.phrs.2021.106039.

Article  CAS  PubMed  Google Scholar 

Jones D. Diabetes field cautiously upbeat despite possible setback for leading SGLT2 inhibitor. Nat Rev Drug Discov. 2011;10:645–6. https://doi.org/10.1038/nrd3546.

Article  CAS  PubMed  Google Scholar 

Marilly E, Cottin J, Cabrera N, et al. SGLT2 inhibitors in type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials balancing their risks and benefits. Diabetologia. 2022;65:2000–10. https://doi.org/10.1007/s00125-022-05773-8.

Article  CAS  PubMed  Google Scholar 

Dadey DYA, Rodrigues A, Haider G, et al. Impact of socio-economic factors on radiation treatment after resection of metastatic brain tumors: trends from a private insurance database. J Neurooncol. 2022;158:445–51. https://doi.org/10.1007/s11060-022-04031-6.

Article  PubMed  Google Scholar 

Clinformatics® Data Mart. https://cdn-aem.optum.com/content/dam/optum4/resources/pdf/clinformatics-data-mart.pdf. Accessed 26 Jan 2023.

Suissa K, Schneeweiss S, Lin KJ, et al. Validation of obesity-related diagnosis codes in claims data. Diabetes Obes Metab. 2021;23:2623–31. https://doi.org/10.1111/dom.14512.

Article  PubMed  PubMed Central  Google Scholar 

Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9. https://doi.org/10.1097/01.mlr.0000182534.19832.83.

Article  PubMed  Google Scholar 

NCI Comorbidity Index Overview. https://healthcaredelivery.cancer.gov/seermedicare/considerations/comorbidity.html. Accessed 25 Aug 2023

Segal Z, Kalifa D, Radinsky K, et al. Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrol. 2020;21:518. https://doi.org/10.1186/s12882-020-02093-0.

Article  PubMed  PubMed Central  Google Scholar 

Box-Steffensmeier JM, Jones BS. Event history modeling: a guide for social scientists. Cambridge: Cambridge University Press; 2004.

Book  Google Scholar 

Surakasula A, Nagarjunapu GC, Raghavaiah KV. A comparative study of pre- and post-menopausal breast cancer: risk factors, presentation, characteristics and management. J Res Pharm Pract. 2014;3:12–8. https://doi.org/10.4103/2279-042X.132704.

Article  PubMed  PubMed Central  Google Scholar 

Biglia N, Peano E, Sgandurra P, et al. Body mass index (BMI) and breast cancer: impact on tumor histopatologic features, cancer subtypes and recurrence rate in pre and postmenopausal women. Gynecol Endocrinol. 2013;29:263–7. https://doi.org/10.3109/09513590.2012.736559.

Article  PubMed  Google Scholar 

Chollet-Hinton L, Anders CK, Tse C-K, et al. Breast cancer biologic and etiologic heterogeneity by young age and menopausal status in the Carolina Breast Cancer Study: a case-control study. Breast Cancer Res. 2016;18:79. https://doi.org/10.1186/s13058-016-0736-y.

Article  PubMed  PubMed Central  Google Scholar 

Hicks B, Kaye JA, Azoulay L, et al. The application of lag times in cancer pharmacoepidemiology: a narrative review. Ann Epidemiol. 2023;84:25–32. https://doi.org/10.1016/j.annepidem.2023.05.004.

Article  PubMed  Google Scholar 

Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–61. https://doi.org/10.1002/pst.433.

Article  PubMed  Google Scholar 

Bergstralh EJ, Kosanke JL, Jacobsen SJ. Software for optimal matching in observational studies. Epidemiology. 1996;7:331–2.

CAS  PubMed  Google Scholar 

Lin DY, Wei LJ. The robust inference for the Cox proportional hazards model. J Am Stat Assoc. 1989;84:1074–8. https://doi.org/10.1080/01621459.1989.10478874.

Article  Google Scholar 

Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med. 2013;32:2837–49. https://doi.org/10.1002/sim.5705.

Article  PubMed  Google Scholar 

Zou H-T, Yang G-H, Cai Y-J, et al. Are high- or low-dose SGLT2 inhibitors associated with cardiovascular and respiratory adverse events? A meta-analysis. J Cardiovasc Pharmacol. 2022;79:655. https://doi.org/10.1097/FJC.0000000000001222.

Article  CAS  PubMed  Google Scholar 

McMurray JJV, Solomon SD, Inzucchi SE, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019;381:1995–2008. https://doi.org/10.1056/NEJMoa1911303.

Article  CAS  PubMed  Google Scholar 

Suissa M, Yin H, Yu OHY, et al. Sodium–glucose cotransporter 2 inhibitors and the short-term risk of breast cancer among women with type 2 diabetes. Diabetes Care. 2020;44:e9–11. https://doi.org/10.2337/dc20-1073.

Article  CAS  PubMed 

留言 (0)

沒有登入
gif