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

Abstract

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.

Screening of dermatology drugs for aberrant use‐patterns: An application of epidemiologic estimates and measures of inequality in drug use

“…databases containing information on individual drug users are increasingly used for screening and evaluating unknown drug effects. [4][5][6][7][8] In addition, evaluating drug utilization patterns by epidemiological measures and by assessment of inequality in drug use may be an important tool for identifying inappropriate or unexpected drug use.…”

“…Occasionally, suboptimal or wrongful use is less obvious and may only be discovered by intentional efforts such as surveillance or screening 3 . Real‐world healthcare databases containing information on individual drug users are increasingly used for screening and evaluating unknown drug effects 4–8 . In addition, evaluating drug utilization patterns by epidemiological measures and by assessment of inequality in drug use may be an important tool for identifying inappropriate or unexpected drug use.…”

Hesperidin, vanillic acid, and sinapic acid attenuate atorvastatin‐induced mitochondrial dysfunction via inhibition of mitochondrial swelling and maintenance of mitochondrial function in pancreas isolated mitochondria

“…Previous studies have been reported diabetogenic effects of several drug classes such as, glucocorticoids, β-blockers, second-generation antipsychotics, thiazides, and statins (Lund et al, 2023). Statins are the gold standard prevention of cardiovascular diseases and treatment of dyslipidemia, which are associated with increase in the incidence of type 2 diabetes in treated individuals (Galicia-Garcia et al, 2020;Needamangalam Balaji et al, 2022;Robinson, 2015).…”

“…Environmental, occupational, and medicinal exposures with diabetogenic agents have been associated with increased risk of type 2 diabetes (Filippini et al, 2022; Jagai et al, 2020; Kolb & Martin, 2017; Leso et al, 2017). Previous studies have been reported diabetogenic effects of several drug classes such as, glucocorticoids, β‐blockers, second‐generation antipsychotics, thiazides, and statins (Lund et al, 2023). Statins are the gold standard prevention of cardiovascular diseases and treatment of dyslipidemia, which are associated with increase in the incidence of type 2 diabetes in treated individuals (Galicia‐Garcia et al, 2020; Needamangalam Balaji et al, 2022; Robinson, 2015).…”

Trend adjustment of the sequence difference when performing sequence symmetry analyses

“…The sequence difference is an absolute measure and conveys the potential public health impact of a given association. Hitherto, this difference has been calculated from the observed data, 4,5 but this approach may be biased if not adjusted for the null-effect SR. Therefore, we propose to calculate an adjusted difference.…”