Identifying diabetogenic drugs using real world health care databases: A Danish and Australian symmetry analysis

Aims: Drug-induced diabetes is underreported in conventional drug safety monitoring and may contribute to the increasing incidence of type 2 diabetes. Therefore, we used routinely collected prescription data to screen all commonly used drugs for diabetogenic effects. Methods: Leveraging the Danish nationwide health registries, we used a case-only symmetry analysis design to evaluate all possible associations between drug initiation and subsequent diabetes. The study was conducted among individuals aged ≥40 years with a first-ever prescription for any antidiabetic drug 1996-2018 (n = 348 996). Sequence ratios (SRs) and 95% confidence intervals (CIs) were obtained for all possible drug class-diabetes combinations. A lower bound of the 95% CI >1.00 was considered a signal. Signals generated in Denmark were replicated using the Services Australia, Pharmaceutical Benefits Scheme 10% data extract. Results: Overall, 386 drug classes were investigated, of which 70 generated a signal. In total, 43 were classified as previously known based on the SIDER database or a literature review, for example, glucocorticoids (SR 1.67, 95% CI 1.62-1.72) and β-blockers (SR 1.20, 95% CI 1.16-1.23). Of 27 new signals, three drug classes yielded a signal in both the Danish and Australian data source: digitalis glycosides (SR 2.15, 95% CI 2.04-2.27, and SR 1.76, 95% CI 1.50-2.08), macrolides (SR 1.20, 95% CI 1.16-1.24, and SR 1.11, 95% CI 1.06-1.16) and inhaled β2-agonists combined with glucocorticoids (SR 1.35, 95% CI 1.28-1.42, and SR 1.14, 95% CI 1.06-1.22). Conclusion: We identified 70 drug-diabetes associations, of which 27 were classified as hitherto unknown. Further studies evaluating the hypotheses generated by this work are needed, particularly for the signal for digitalis glycosides.