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Relationship Between Melatonin Receptor Agonists and Parkinson's Disease.

Yoshihiro NoguchiRikuto MasudaHaruka AizawaTomoaki Yoshimura
Published in: Journal of pineal research (2024)
Parkinson's disease affects millions of people worldwide, and without significant progress in disease prevention and treatment, its incidence and prevalence could increase by more than 30% by 2030. Researchers have focused on targeting sleep and the circadian system as a novel treatment strategy for Parkinson's disease. This study investigated the association between melatonin receptor agonists and Parkinson's disease, using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS). The target drugs were melatonin receptor agonists including ramelteon, tasimelteon, and agomelatine. Parkinson's disease cases were defined according to the Medical Dictionary for Regulatory Activities (MedDRA) 25.0; Standardized MedDRA Query (SMQ) using both the "narrow" and "broad" preferred terms (PTs) associated with Parkinson's disease. The association between melatonin receptor agonists (ramelteon, tasimelteon, and agomelatine) and Parkinson's disease was evaluated by the reporting odds ratio. Upon analyzing the data from all patients registered in the FAERS, ramelteon (ROR: 0.66, 95% confidence interval [95% CI]: 0.51-0.84) and tasimelteon (ROR: 0.49, 95% CI: 0.38-0.62) showed negative correlations with Parkinson's disease. Conversely, only agomelatine was positively correlated with Parkinson's disease (ROR: 2.63, 95% CI: 2.04-3.40). These results suggest that among the melatonin receptor agonists, ramelteon and tasimelteon are negatively correlated with Parkinson's disease. In contrast, agomelatine was shown to be positively correlated with Parkinson's disease. These results should be used in research to develop drugs for the treatment of Parkinson's disease, fully considering the limitations of the spontaneous reporting system.
Keyphrases
  • healthcare
  • emergency department
  • risk factors
  • computed tomography
  • magnetic resonance imaging
  • physical activity
  • transcription factor
  • newly diagnosed
  • prognostic factors
  • single molecule
  • deep learning