THN 102 for Excessive Daytime Sleepiness Associated with Parkinson's Disease: A Phase 2a Trial

An overview of the effects of levodopa and dopaminergic agonists on sleep disorders in Parkinson's disease

Sleep disorders are among the most common nonmotor symptoms in Parkinson's disease and are associated with reduced cognition and health-related quality of life. Disturbed sleep can often present in the prodromal or early stages of this neurodegenerative disease, rendering it crucial to manage and treat these symptoms. Levodopa and dopaminergic agonists are frequently prescribed to treat motor symptoms in Parkinson's disease, and there is increasing interest in how these pharmacological agents affect sleep and their effect on concomitant sleep disturbances and disorders. In this review, we discuss the role of dopamine in regulating the sleep-wake state and the impact of neurodegeneration on sleep. We provide an overview of the effects of levodopa and dopaminergic agonists on sleep architecture, insomnia, excessive daytime sleepiness, sleep-disordered breathing, rapid eye movement sleep behavior disorder, and restless legs syndrome in Parkinson's disease. Levodopa and dopaminergic drugs may have different effects, beneficial or adverse, depending on dosing, method of administration, and differential effects on the different dopamine receptors. Future research in this area should focus on elucidating the specific mechanisms by which these drugs affect sleep in order to better understand the pathophysiology of sleep disorders in Parkinson's disease and aid in developing suitable therapies and treatment regimens.

CITATION

Scanga A, Lafontaine A-L, Kaminska M. An overview of the effects of levodopa and dopaminergic agonists on sleep disorders in Parkinson's disease. J Clin Sleep Med. 2023;19(6):1133-1144.

Parkinson's disease - current treatment

PURPOSE OF REVIEW

The purpose is to review the results and impact of recent studies for current and future treatment of both motor and non-motor symptoms in Parkinson's disease (PD).

RECENT FINDINGS

New formulations of levodopa further optimize motor fluctuations, allowing for more on-time and less dyskinesia. On demand apomorphine continues to showcase itself as an effective and tolerable tool for treating motor off-periods. Though there are no clear treatment guidelines for PD-related constipation and sleep related disorders, several new agents for these non-motor symptoms show promising preliminary data. Expiratory muscle strength training may represent a useful and cost-effective strategy to alleviate oropharyngeal dysphagia associated with PD. There is evidence to suggest that the use of shorter pulse width and directional deep brain stimulation leads can results in a greater therapeutic window.

SUMMARY

Though no interventions currently exist to significantly modify the disease progression of PD, new studies continue to give insight into optimal symptomatic management. Clinicians should be familiar with expanding the repertoire of tools available to treat the diverse range of symptoms and challenges associated with PD.

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

Objective

Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).

Materials and methods

A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.

Results

At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.

Conclusion

In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.