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Anti-diabetic drug use and reduced risk of Parkinson's disease: A community-based cohort study
Background: Emerging evidence suggests a potential association between certain anti-diabetic drugs and a reduced risk of Parkinson's disease (PD). Limited population-based studies have investigated users of newer anti-diabetic drugs such as GLP-1 agonists or SGLT2 inhibitors. Objective: The aim of this study was to assess the risk of PD among individuals with type 2 diabetes mellitus (T2DM) who were treated with various types of anti-diabetic drugs over time.
Methods: A population-based cohort comprising T2DM patients aged over 30 who used metformin, GLP-1 agonists, thiazolidinediones, sulfonylureas, DPP4 inhibitors, SGLT2 inhibitors, or meglitinides between January 1, 1999 and December 31, 2018. Data were obtained between the diabetes registration and drug purchase databases of Maccabi Healthcare Services. Time-dependent Cox regression models, adjusted for sex, age, and comorbidities were employed to calculate the adjusted haza rd ratios (HRs) for the PD risk associated with different anti-diabetic drugs over time.
Results: The study population comprised 86,229 T2DM patients, with 53.9 % males. The mean age at the first anti-diabetic drug purchase was 59.0 ± 11.0 and 62.0 ± 11.0 years for men and women respectively. Compared to metformin, several drug types were associated with a significantly lower PD risk: thiazolidinediones (HR = 0.91, 95 % CI:0.074–1.14); DPP4 inhibitors (HR = 0.60, 95 % CI:0.53–0.67); meglitinides (HR = 0.63, 95 % CI:0.53–0.74); GLP-1 agonists (HR = 0.54, 95 % CI:0.39–0.73); and SGLT2 inhibitors (HR = 0.15, 95 % CI:0.10–0.21). Conclusions: Our results suggest a reduced risk of PD with certain anti-diabetic drugs, particularly SGLT2 inhibitors and GLP-1 agonists. Validation through extensive big-data studies is essential to confirm these results and to optimize PD prevention and management. © 2024 Elsevier Ltd
Authors : Rozani V.; Bezimianski M.G.; Azuri J.; Bitan M.; Peretz C.
Source : Elsevier Ltd
Article Information
| Year | 2024 |
| Type | Article |
| DOI | 10.1016/j.parkreldis.2024.107132 |
| ISSN | 13538020 |
| Volume | 128 |
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