GRAPHICAL ABSTRACT

INTRODUCTION

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by neuronal loss and the presence of Lewy pathology, which results in abnormally misfolded α-synuclein in both dopaminergic and nondopaminergic brain areas, causing motor and nonmotor symptoms (NMSs) [1]. Motor symptoms are predominantly a consequence of dopaminergic neuron loss in the substantia nigra pars compacta, and pharmacological treatment of PD focuses on dopamine replacement strategies [2]. However, dopaminergic therapy showed limited efficacy on NMS [2].

Sleep disturbance is among the most common NMSs in PD patients, with wide variability in its prevalence rate [3]; it can appear in the prodromal phase of PD several years before the onset of motor symptoms and can continue to develop throughout the progression of PD [4]. Although sleep problems negatively affect patient outcomes, including their quality of life, these issues have typically been underreported by patients and underrecognized by clinicians [4]. Moreover, most pharmacologic and nonpharmacologic intervention studies for sleep disturbances in PD patients are limited by insufficient evidence regarding efficacy; practical implications remain largely investigational or are only possibly useful according to the International Parkinson and Movement Disorder Society’s evidence-based medicine [5]. There remain considerable unmet needs for the management of sleep disturbances in patients with PD.

Two adjunctive medications widely prescribed for the management of motor symptoms in PD patients are monoamine oxidase-B (MAO-B) inhibitors and catechol-O-methyltransferase (COMT) inhibitors [6,7]. MAO-B inhibitors increase dopaminergic activity by inhibiting dopamine breakdown within the central nervous system, whereas COMT inhibitors act peripherally to reduce levodopa degradation, thereby prolonging its therapeutic effect [6,7]. In recent years, accumulated evidence has suggested that MAO-B and COMT inhibitors may have beneficial effects not only on motor symptoms but also on NMSs, specifically sleep disturbances [8-10]. Despite these emerging observations, robust, head-to-head randomized controlled trials (RCTs) are lacking, making it difficult to draw comprehensive conclusions regarding their comparative efficacy and safety in managing PD patient sleep problems.

This study included a network meta-analysis (NMA) to compare and evaluate the overall effects of MAO-B and COMT inhibitors on sleep disturbances in patients with PD. Given their distinct pharmacological properties, we focused specifically on MAO-B and COMT inhibitors to ensure a mechanistically and analytically coherent comparison. Other classes of dopaminergic therapies—such as long-acting levodopa formulations, dopamine agonists, and device-based infusion or patch systems—were excluded because their heterogeneous administration routes and distinct pharmacodynamics would compromise the network’s consistency and the biological interpretability of the results. The goal of this focused NMA was to leverage direct and indirect comparisons to generate evidence-based insights that can help optimize treatment strategies for PD patients with sleep problems, thereby shifting the therapeutic focus toward personalized interventions for these critical NMSs.

MATERIALS & METHODS

This systematic review and NMA were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), including extensions for NMA [11]. The study protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO; registration No. CRD420251013028).

Data sources and literature search

A comprehensive literature search was performed using PubMed, Embase, and the Cochrane Library from the respective inception date to April 30, 2025, using Medical Subject Headings (MeSH) terms and general text keywords. The search terms were grouped through Boolean operators (e.g., AND, OR, and NOT). Subject headings and text keywords were associated with improvements in sleep parameters after treatment with MAO-B and COMT inhibitors in patients with PD.

There were no restrictions on language or study design, but publications that did not contain original research findings (e.g., conference abstracts, case reports, case series, review articles, editorials, letters, and guidelines) were excluded. Two independent researchers (YJ Jung and SR Shim) conducted a literature search based on a valid, established strategy and manually checked clinical trial registries and Google Scholar to increase the sensitivity of additional searches. The full search strategies are listed in Supplementary Table 1.

Study selection

The inclusion criteria were as follows: 1) studies including patients with a clinical diagnosis of PD; 2) interventions/comparisons consisting of MAO-B inhibitors (safinamide, rasagiline, or selegiline) or COMT inhibitors (opicapone, entacapone, or tolcapone); 3) outcome measures consisting of improvements in subjective or objective sleep quality parameters; and 4) RCTs or cohort studies as research designs.

To establish the integrity of the collected research, multiple researchers independently reviewed and cross-checked the literature and relevant references. Disagreements among researchers during the literature collection process were resolved through a panel discussion among all researchers, and the final collected literature was selected after agreement by the entire research group.

Data extraction

Basic details about the studies (first author, year of publication, and study design), patient characteristics (age, male percentage, and number of patients), details of the interventions and comparisons (name, dosage, and duration of medication administration), and outcome variables were extracted from the included articles using a predefined data extraction form. The final NMA included only studies that provided comprehensive and complete information.

Statistical analysis

We conducted an NMA using the “netmeta” package in R software (version 4.3.1; The R Foundation). This package implements a frequentist approach based on graph-theoretical methods analogous to electrical network theory [12]. The package accommodates both arm-level and contrast-level data, adjusting for within-study correlations in multiarm trials by recalculating variances using the Laplacian matrix and its pseudoinverse [13,14]. Given the observed heterogeneity among studies, we applied random-effects models to allow generalization beyond the included studies, assuming that they represent a random sample from a larger population [15]. For outcomes in which a closed loop was formed in the network, network consistency was evaluated using node-splitting and design-based decomposition techniques. The latter approach classifies trials based on the treatment subsets they compare and assesses result consistency across these classifications. To facilitate treatment ranking interpretation, we computed the surface under the cumulative ranking curve (SUCRA), where higher values suggest potentially superior interventions [13]. We calculated pooled standardized mean differences (SMDs) or mean differences (MDs) with their corresponding 95% confidence intervals (CIs). Statistical significance was considered when the two-sided p value was ≤0.05.

Assessment of potential publication bias

Publication bias was analyzed using funnel plots. A funnel plot is a schematic diagram of the SMDs or MDs of the parameters for subjective or objective sleep quality measures. If there was no publication bias, individual studies were symmetrically distributed at the top of the funnel; otherwise, they were relatively distributed outside the funnel if they were asymmetric, indicating potential publication bias. In addition, summary statistics of publication bias were also tested using Egger’s test [12,14,16].

Quality assessment

The Risk of Bias 2.0 (RoB) tool, developed by the Cochrane Collaboration, serves as a method for assessing the methodological quality of RCTs [17]. This tool assesses five domains of potential bias: 1) bias arising from randomization, 2) bias due to departure from the intended intervention, 3) bias due to missing outcome data, 4) bias in outcome measurement, and 5) bias in the selection of outcomes reported. Each domain was classified as “low,” “some concern,” or “high” risk of bias.

For observational studies, the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) was used to evaluate the risk of bias [18]. The evaluation domain consists of three major parts (i.e., before, during, and after the intervention) and consists of a total of seven items (i.e., preintervention: 1) bias due to confounding, 2) bias in the selection of participants into the study; at intervention: 3) bias in the classification of interventions; postintervention: 4) bias due to deviations from intended interventions, 5) bias due to missing data, 6) bias in the measurement of outcomes, and 7) bias in the selection of the reported results). The quality of the evidence related to the estimation of benefits and risks is displayed according to specific conditions.

RESULTS

Identification of studies

A total of 529 records were identified through a database search (PubMed, n=57; Cochrane Library, n=189; Embase, n=274) and an additional manual search (n=9). After performing duplicate removal via automated tools (n=217) and manual review (n=176), 136 records remained. After screening the titles and abstracts, we excluded an additional 81 records. Among the remaining 55 articles, 43 were excluded for the following reasons: unrelated to the intervention (n=39), not targeting condition/disease (n=2), and an absence of outcome measures (n=2). Among the remaining 12 full-text articles, 5 studies were excluded due to the absence of quantified outcomes (n=3) or a comparison group (n=2). Overall, 7 studies met the selection criteria for qualitative and quantitative synthesis (Figure 1).

We conducted a systematic literature review and NMA of 7 studies comparing various interventions: 4 RCTs [19-22], 2 prospective observational studies [23,24], and 1 retrospective observational study [25]. A detailed summary of the study characteristics, including the inclusion criteria and outcome measures, is presented in Table 1. The included studies were published between 2016 and 2024. The study population primarily consisted of patients with PD who had sleep disturbances or motor fluctuations, while two studies [19, 21] included untreated early-stage PD patients. Interventions varied across studies and included MAO-B inhibitors (rasagiline and safinamide), COMT inhibitors (entacapone and opicapone), a combination of rasagiline and pramipexole (P2B001), and a placebo. The follow-up periods ranged from 12 to 16 weeks. Sleep-related outcomes included Epworth Sleepiness Scale (ESS) scores, Parkinson’s Disease Sleep Scale (PDSS) scores, and polysomnographic (PSG) parameters. Across studies, sample sizes ranged from 15 to 157 participants, with mean ages generally in the mid-to-late 60s. The proportion of male participants varied from 52.6% to 76.7%, indicating a predominance of males across the included studies.

Network meta-analysis of the effects of MAO-B and COMT inhibitors on subjective sleep in PD patients

Compared with the placebo, neither the MAO-B nor the COMT inhibitor significantly improved subjective overall sleep quality (Figure 2A). Compared with placebo, 50 mg of opicapone had the greatest SMD (-0.26 [95% CI, -2.68 to 2.16]), suggesting a potential benefit; however, the wide CI encompassing zero indicates that the effect was not statistically significant. The ranking probabilities indicated that 50 mg opicapone had the highest likelihood of being the most effective treatment, followed by placebo and 200 mg entacapone. Despite these trends, the overall evidence remains inconclusive due to the lack of statistical significance.

As depicted in Figure 2B, compared with placebo, rasagiline (SMD, -2.13 [-4.99 to 0.73]), safinamide (SMD, -1.82 [-4.69 to 1.05]), and rasagiline extended release (ER) 0.75 mg (SMD -0.60 [-5.78 to 4.58]) were associated with improvements in daytime sleepiness. However, none of these comparisons reached statistical significance. In contrast, compared with placebo, pramipexole ER, alone or in combination with other agents, tended to worsen daytime sleepiness, although these results also lacked statistical significance. Treatment ranking suggests that rasagiline has the highest probability of being the most beneficial in reducing daytime sleepiness.

Network meta-analysis of the effects of MAO-B inhibitors on objective sleep in PD patients

Given that only a small subset of the included studies provided PSG data, forming a sparse evidence network, the following NMA of objective sleep parameters should be considered exploratory. As depicted in Figure 3, indirect comparisons in the network model suggest that safinamide demonstrates a statistically significant reduction in wake after sleep onset (WASO) (MD, -10.20 min [95% CI, -19.38 to -1.02]), indicating a clinically meaningful improvement in sleep continuity through a reduction in nighttime awakening. Safinamide also significantly increased rapid eye movement (REM) sleep duration (MD, 5.70 min [95% CI, 2.26 to 9.14]), suggesting a potential benefit in enhancing REM sleep. Rasagiline was associated with an increase in REM and N1 sleep duration and a decrease in N3 sleep duration. No other comparisons for total sleep time (TST), sleep efficiency (SE), sleep latency (SL), or the periodic limb movement (PLM) index reached statistical significance. The inconsistency tests for NMA were analyzed using the node-splitting approach, and the findings (p>0.05 for each) indicated consistency across the direct and indirect comparisons of all outcomes.

Assessment of publication bias

The statistical approaches used for the detection of publication bias or a small-study effect for each outcome are shown in Supplementary Figure 1. Overall sleep and daytime sleepiness were symmetrical, and there was no indication of publication bias. REM sleep and WASO, which were included in a small number of studies, were excluded from the publication bias analysis, and none showed publication bias according to Egger’s regression tests or Begg and Mazumdar rank tests (p>0.05 for each).

Quality assessment

Four RCTs [19-22] were evaluated using the Cochrane RoB 2.0 tool, which assesses the risk of bias across five domains (Supplementary Figure 2A). They were judged to have an overall “low” risk of bias across all domains. Among the three non-RCTs assessed using the ROBINS-I tool (Supplementary Figure 2B), the study by Liguori et al. [25] was judged to have a “low” risk of bias across all domains. In contrast, the study by Plastino et al. [24] was assessed as having a “serious” overall risk of bias, with moderate risk in the selection of participants (D2) and no information available for several domains (D1, D4, D7). The study by Bovenzi et al. [23] was rated as having a “critical” overall risk of bias, mainly due to critical confounding (D1), moderate selection bias (D2), and a lack of information regarding deviations from intended interventions and missing data (D4, D5).

DISCUSSION

The aim of this NMA was to compare and evaluate the effects of MAO-B and COMT inhibitors on subjective and objective sleep parameters in patients with PD. While no treatment significantly improved subjective sleep quality outcomes, safinamide had statistically significant favorable effects on two key objective sleep parameters—REM sleep duration and WASO—indicating its potential utility in managing sleep disruption in PD patients. However, other PSG parameters, including sleep stage proportions and PLM indices, did not significantly change. These findings should be interpreted as hypothesis-generating and not as confirmatory evidence.

In the analysis of subjective overall sleep measures, compared with the placebo, none of the MAO-B or COMT inhibitors significantly improved sleep. Nonetheless, compared with placebo, 50 mg of opicapone improved overall sleep; this aligns with the recent single-arm OASIS (OpicApone in Sleep dISorder) trial, which reported a statistically significant and clinically meaningful improvement in its primary endpoint, the PDSS-2 total score (mean change: -7.9, p=0.0099), after 6 weeks of treatment with 50 mg opicapone as a dopaminergic adjuvant treatment in patients with PD, motor fluctuation, and sleep disturbance [26]. These findings are also consistent with the results of a post hoc analysis of the pivotal opicapone trials BIPARK-I and II, which analyzed pooled diary data to assess OFF-time, nighttime sleep duration and awake periods and demonstrated that adding opicapone to a levodopa regimen significantly reduced the total time spent awake at night in patients with night-time “OFF” compared to placebo [10]. Furthermore, in a phase IV real-world study (OPTIPARK) conducted across the UK and Germany, improvements in NMS, including sleep/fatigue, were observed after 3 months of treatment with opicapone when it was added to standard levodopa therapy [27].

Similarly, although none of the treatments demonstrated statistically significant effects, a numerical trend favoring rasagiline, safinamide, and rasagiline ER 0.75 mg over placebo was observed in the analysis of subjective daytime sleepiness measures. In contrast, compared with placebo, all pramipexole ER-containing regimens tended to worsen daytime sleepiness. While these findings did not reach statistical significance, the consistent direction of the effect is clinically relevant. Given that excessive daytime sleepiness is a well-recognized adverse effect of pramipexole [3], these results may reinforce existing clinical concerns and support the careful selection of dopaminergic adjuncts in patients with PD, particularly those who are vulnerable to sleep-related complications. However, these effects failed to reach statistical significance, emphasizing the need for cautious interpretation and highlighting the persistent limitations in subjective sleep evaluation methods, which may be prone to reporting bias or a lack of sensitivity in capturing nuanced changes.

For objective sleep parameters derived from PSG, safinamide consistently demonstrated the most promising profile. It significantly reduced the WASO, which is a clinically meaningful finding, as nocturnal awakening contributes substantially to fragmented sleep and daytime dysfunction. Additionally, safinamide significantly increased REM sleep duration, suggesting that its sleep-promoting effects may extend to restoring essential physiological sleep stages. These results align with those of prior real-world studies, such as the SAFINONMOTOR and VALE-SAFI trials, which also reported improvements in objective and subjective sleep parameters in patients receiving safinamide [8,9]. In contrast, rasagiline did not significantly improve PSG parameters, although it exhibited a numerical trend toward increased N2 and REM sleep. Interestingly, both safinamide and rasagiline were associated with minimal or no benefit in N3 sleep and a decrease in N1 sleep, suggesting that neither agent meaningfully altered the overall sleep architecture. Furthermore, compared with placebo, only safinamide reduced the PLM index, whereas rasagiline worsened it. Although these changes in PLM were not statistically significant, they may indicate an additional advantage of safinamide in reducing motor-related sleep disruptions. While these findings are promising, they must be interpreted with significant caution, as they were derived from only two observational studies with small sample sizes (n=30 and n=45) and variable risk of bias. Therefore, these results should be viewed as hypothesis-generating rather than conclusive, and they serve primarily to highlight the critical need for more large-scale, head-to-head RCTs that utilize objective PSG measures to confirm these potential benefits.

Safinamide, a third-generation, selective, and reversible MAOB inhibitor, has a more complex mechanism of action than older irreversible MAO-B inhibitors [28]. Safinamide notably exerts additional effects beyond dopaminergic modulation by inhibiting glutamate release via sodium and N-type calcium channel blockade [28]. Since glutamate plays a key role in promoting wakefulness, its suppression has been linked to improved sleep quality, reduced nighttime awakenings, and enhanced sleep continuity [29]. Additionally, thalamic glutamatergic hyperactivation, iron dysregulation and dopaminergic dysfunction contribute to the occurrence of PLM in patients with PD [30]. Based on these data, the ability of safinamide to modulate glutamatergic transmission may help improve sleep-related parameters in patients with PD.

The clinical implications of this study are the significant effects of safinamide on REM sleep and WASO, suggesting that it may offer dual benefits in PD by improving both motor and nonmotor domains, including objective sleep parameters. Given the heterogeneity of sleep disturbances in PD patients and the complexity of their underlying mechanisms, individualized treatment approaches based on specific sleep phenotypes may be warranted.

However, several limitations should be acknowledged. First, the inclusion of only seven studies—four of which were RCTs—reflects a scarcity of high-quality, sleep-related outcome data for MAO-B or COMT inhibitors in patients with PD. The small sample sizes and limited number of studies reduce the statistical power of the analysis and may not fully satisfy the normality assumption required for a random-effects model. This could lead to less precise or potentially biased estimates; thus, our findings, particularly those from the NMA, should be interpreted with caution. To mitigate this concern, we also conducted a Bayesian NMA, which confirmed the robustness of our frequentist results. Second, the assessment of network consistency was limited. While a node-splitting analysis confirmed consistency for two outcomes that formed a closed loop (e.g., overall sleep, daytime sleepiness), most outcomes did not, precluding a formal analysis of this key NMA assumption and representing a notable limitation. Third, while the quality of the included evidence was generally high for four RCTs, two non-RCTs exhibited a severe to critical risk of bias, particularly due to confounding and incomplete data. This limitation necessitates caution in interpreting the results of observational studies and emphasizes the importance of well-designed RCTs. Moreover, publication bias was not evident in the funnel plot analyses for the major outcomes, although the limited study numbers for PSG measures such as REM and WASO restrict the interpretability of these assessments. Fourth, variability in PD stages, types and dosages of MAO-B or COMT inhibitors, and follow-up periods can complicate direct and indirect comparisons. Finally, our review focused only on MAO-B and COMT inhibitors and excluded other relevant drug classes, such as dopamine agonists, long-acting levodopa formulations, and infusion or patch-based therapies. This decision, while necessary to maintain methodological homogeneity and ensure the validity of the network comparison, limits the scope of our conclusions. Future NMAs incorporating broader therapeutic classes, once sufficiently homogeneous data become available, will be essential to provide a more comprehensive overview of pharmacologic strategies for managing sleep disturbances in PD patients.

In conclusion, evidence has shown that safinamide is significantly effective for improving objective sleep parameters in patients with PD. These results can inform clinical decision-making regarding dopaminergic adjunctive therapy selection for PD patient sleep disturbances. However, given the paucity of evidence and controlled, long-term studies, the effects of MAO-B and COMT inhibitors on sleep disturbances in PD patients remain unclear. More high-quality studies are needed and should enable clinicians to provide personalized medicine based on sleep profiles.

Supplementary Materials

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Statement

This work was supported by Konyang University Myunggok Research Fund of 2023.

Acknowledgments

None

Author Contributions

Conceptualization: Seon-Min Lee, Sung Ryul Shim, Kyum-Yil Kwon, Yu Jin Jung. Data curation: Seon-Min Lee, Sung Ryul Shim, Kyum-Yil Kwon, Yu Jin Jung. Formal analysis: Sung Ryul Shim. Funding acquisition: Seon-Min Lee. Investigation: Seon-Min Lee, Sung Ryul Shim, Yu Jin Jung. Methodology: Sung Ryul Shim. Project administration: Seon-Min Lee, Sung Ryul Shim, Kyum-Yil Kwon, Yu Jin Jung. Resources: Seon-Min Lee, Sung Ryul Shim, Yu Jin Jung. Software: Sung Ryul Shim. Supervision: Sung Ryul Shim, Yu Jin Jung. Validation: Sung Ryul Shim. Visualization: Sung Ryul Shim, Yu Jin Jung. Writing—original draft: Seon-Min Lee, Yu Jin Jung. Writing—review — editing: Sung Ryul Shim, Kyum-Yil Kwon, Taeho Greg Rhee.

Figure 1.

PRISMA flowchart.

Figure 2.

Network meta-analysis results and SUCRA values for subjective sleep. PP_ER6, pramipexole ER 0.6 mg; PP_ER3_rasagiline ER 0.75, pramipexole ER 0.3 mg + rasagiline ER 0.75 mg; PP_ER6_rasagiline ER 0.75, pramipexole ER 0.6 mg + rasagiline ER 0.75 mg. A: Overall sleep. B: Daytime sleepiness. SMD, standardized mean difference; CI, confidence interval; SUCRA, surface under the cumulative ranking curve.

Figure 3.

Network meta-analysis results and SUCRA values for objective sleep. TST, total sleep time; SE, sleep efficiency; SL, sleep latency; WASO, wake after sleep onset; MD, mean difference; CI, confidence interval; PLM, periodic limb movements; SUCRA, surface under the cumulative ranking curve.

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