Sleep in Parkinson's disease: A systematic review and meta-analysis of polysomnographic findings

Journal Pre-proof Sleep in Parkinson's disease: A systematic review and meta-analysis of polysomnographic findings Ye Zhang, Rong Ren, Larry D. Sanford, Linghui Yang, Junying Zhou, Lu Tan, Taomei Li, Jihui Zhang, Yun-Kwok Wing, Jie Shi, Lin Lu, Xiangdong Tang PII:

S1087-0792(20)30024-1

DOI:

https://doi.org/10.1016/j.smrv.2020.101281

Reference:

YSMRV 101281

To appear in:

Sleep Medicine Reviews

Received Date: 10 November 2019 Revised Date:

21 January 2020

Accepted Date: 23 January 2020

Please cite this article as: Zhang Y, Ren R, Sanford LD, Yang L, Zhou J, Tan L, Li T, Zhang J, Wing Y-K, Shi J, Lu L, Tang X, Sleep in Parkinson's disease: A systematic review and meta-analysis of polysomnographic findings, Sleep Medicine Reviews, https://doi.org/10.1016/j.smrv.2020.101281. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Ltd. All rights reserved.

Sleep in Parkinson's disease: A systematic review and meta-analysis of polysomnographic findings Ye Zhang a, 1, Rong Ren a, 1, Larry D. Sanford b,*, Linghui Yang a, Junying Zhou a, Lu Tan a, Taomei Li a, Jihui Zhang c, Yun-Kwok Wing c, Jie Shi d, Lin Lu d, Xiangdong Tang a, * 1

Ye Zhang and Rong Ren contributed equally to this work.

Affiliations a

Sleep Medicine Center, Department of Respiratory and Critical Care Medicine,

Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. b

Sleep Research Laboratory, Center for Integrative Neuroscience and Inflammatory

Diseases, Department of Pathology and Anatomy, Eastern Virginia Medical School, Norfolk, VA, USA. c

Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong

Kong, Shatin, Hong Kong Special Administrative Region of China. d

National Institute on Drug Dependence, Peking University Sixth Hospital, Peking

University, Beijing 100191, China.

*Corresponding authors Xiangdong Tang, Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Dian Xin Nan Jie 28#, Chengdu 610041, China. E-mail address: [email protected] Larry D. Sanford, Sleep Research Laboratory, Center for Integrative Neuroscience and Inflammatory Diseases, Department of Pathology and Anatomy, Eastern Virginia Medical School, P.O. Box 1980, Norfolk, VA 23507; Tel: (757) 446-7081; Fax: (757) 446-5719. Email address: [email protected] 1

SUMMARY

Polysomnographic studies have been conducted to explore sleep changes in Parkinson’s disease (PD), but the relationships between sleep disturbances and PD are imperfectly understood. We conducted a systematic review of the literature exploring polysomnographic differences between PD patients and controls in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycIFNO. 67 studies were identified for systematic review, 63 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, N2 percentage, slow wave sleep, rapid eye movement sleep (REM) percentage, and increases in wake time after sleep onset, N1 percentage, REM latency, apnea hypopnea index, and periodic limb movement index in PD patients compared with controls. There were no remarkable differences in sleep continuity or sleep architecture between PD patients with and without REM sleep behavior disorder (RBD). Our study suggests that PD patients have poor sleep quality and quantity. Sex, age, disease duration, presence of RBD, medication status, cognitive impairment, and adaptation night are factors that contributed to heterogeneity between studies.

Keywords: Parkinson's disease; polysomnography; meta-analysis

2

Abbreviations AASM AHI BDI CIs EEG H&Y LEDD MDS-UPDRS MMSE MoCA NICE OR OSA PD PLMI PLMS PSA PSG R&K RBD REM REML REMD SE SL

American Academy of Sleep Medicine apnea hypopnea index Beck Depression Inventory confidence intervals electroencephalogram Hoehn & Yahr stage levodopa equivalent dose Movement Disorder Society-sponsored revision of the Unified Parkinson Disease Rating Scale Mini-Mental State Examination Montreal Cognitive Assessment National Institute for Health and Care Excellence odds ratio Obstructive sleep apnea Parkinson’s disease Periodic limb movement index Periodic limb movements in sleep power spectral analysis polysomnography Rechtschaffen and Kales REM sleep behavioral disorder rapid eye movement rapid eye movement sleep latency rapid eye movement sleep density sleep efficiency sleep latency

SMD SWS

standardized mean difference slow wave sleep

TST UPDRS

total sleep time Unified Parkinson Disease Rating Scale

WASO

wake time after sleep onset

3

Introduction

Parkinson’s disease (PD), the second most common neurodegenerative disease, is traditionally recognized as a movement disorder and its diagnosis is based on motor signs including rigidity, tremor, and bradykinesia [1, 2]. Although, the management of motor symptoms is important, the non-motor symptoms in PD patients have also been given more attention in recent years [3]. Non-motor symptoms (i.e., neuropsychiatric symptoms, sleep disturbances, autonomic symptoms, gastrointestinal symptoms, sensory symptoms, etc) are associated with decreased quality of life, are common reasons for hospitalization, and result in an almost quadrupling of the cost of patient care [4-9].

Among various non-motor symptoms in PD, sleep disturbances, including daytime sleepiness, insomnia, restless legs syndrome, and rapid eye movement (REM) sleep behavior disorder (RBD), impact over 90% of PD patients; their rates increase during the course of the disease and are related its rapid progression [10-17]. Thus, sleep disturbances in PD are of clinical relevance and are important for understanding its etiology [18-23]. For example, Lima suggested RBD could serve as an early sign of alterations in dopaminergic neurotransmission in medullary and/or pontine REM sleep-related structures that play important roles in PD pathology [11]. Additionally, a recent meta-analysis of longitudinal studies revealed that over 90% of RBD patients converted to a neurodegenerative disorder after a long-term follow-up period [22]. Even after a mean follow-up of 4.75 years, 31.95% of RBD patients converted into a neurodegenerative disorder, of which the most frequent disease is PD (44% of converters) [22], suggesting that RBD could be a prodromal stage of PD [3, 24].

The assessment of sleep disturbances includes polysomnography (PSG), actigraphy, face-to-face interview, self-report questionnaire, and sleep diary. Of these, PSG is required to distinguish rapid eye movement (REM) sleep and non rapid eye movement (NREM) sleep, and stage N1 to N3 of NREM sleep. PSG determined sleep 4

disruptions are useful for understanding the neurobiology of neurodegeneration. For instance, emerging evidence supports a potentially beneficial role of N3 in neurodegeneration [25, 26]. Additionally, REM sleep could contribute to the maintenance of neuronal homeostasis in the brain; REM sleep disturbance impacts brain excitability, synaptic pruning, and neurogenesis, and loss of REM sleep can cause neurodegeneration [27].

Some studies have examined PSG data in attempts to understand sleep alterations associated with PD. However, the exact changes in PSG (i.e., total sleep time (TST), N3, and REM percentage) in PD compared to healthy controls have not been fully established [13, 28]. Variations in findings across different studies may involve heterogeneity in demographic characteristics (i.e., gender and age), clinical variables (i.e., PD severity, PD duration, disease stage, comorbid RBD, and medication status), and experimental methodology (i.e., PSG scoring methods and adaptation). Pooled analyses via meta-analytic techniques can be useful for resolving discrepancies across studies and for estimating the impact of moderators.

Though 15 case-control studies investigating PSG changes in PD patients were discussed in a review in 2012 [28], meta-analytic data exploring the differences in PSG data between PD patients and controls are currently lacking. In addition, our preliminary literature search revealed more than 30 relevant articles published after 2012. Given the significant number of recent studies, we systematically reviewed previous case-control studies, and, where possible, used meta-analytic procedures to identify the pooled effect sizes (and range of credible values) for the differences in PSG variables between PD patients and controls. We also explored moderators which could contribute to heterogeneity across studies.

Methods

Protocol and registration 5

The protocol for this study was registered (PROSPERO ID: CRD42018110040; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=110040)

in

accordance with the preferred reporting items for systematic reviews and meta-analyses statement [29].

Eligibility criteria according to the PICOS approach Participants (P): The participants were adult PD patients according to Brain Bank criteria [30]. We also included studies which did not specify diagnostic criteria for PD but where the methods enabled determining whether PD was determined by physician diagnosis or by clinical symptoms combined with neuroimaging. Studies unrelated to PD, studies that included children, and animal studies were excluded. In addition, studies including patients with comorbid PD and RBD required that RBD was confirmed by PSG. Intervention (I): We included studies which reported that their participants were drug naïve PD patients and studies which included PD patients who were undergoing medication treatments (i.e., antiparkinsonian drugs, benzodiazepines, antidepressants). This was helpful for clarifying the effects of medication on sleep in PD patients. Comparison (C): Comparisons were to healthy controls. Studies not using healthy controls were excluded from the main analyses. However, studies reporting only on PSG differences between PD patients with and without RBD were included in a supplementary analysis aimed at clarifying PSG variations in PD patients with and without RBD. Outcomes (O): The primary outcomes were differences in PSG variables between PD patients and controls. The secondary outcomes were the contributions of demographic, clinical and methodological factors to heterogeneity between studies. These included age, sex, body mass index (BMI), severity of motor symptoms (the Unified Parkinson Disease Rating Scale (UPDRS) part III or the Movement Disorder Society-sponsored revision of the Unified Parkinson Disease Rating Scale (MDS-UPDRS) part III score), PD duration (years), Hoehn & Yahr (H&Y) stage, levodopa equivalent dose (LEDD), using benzodiazepines, using antidepressants, PD 6

severity, daytime sleepiness (Epworth Sleepiness Scale (ESS) score), severity of cognitive impairment (Mini-mental State Examination (MMSE) score), subjective sleep quality (Pittsburgh sleep quality index (PSQI) score), depression symptoms (Beck Depression Inventory (BDI) score), severity of periodic limb movements (periodic limb movement index (PLMI)), exclusion of obstructive sleep apnea (OSA), presence of RBD, adaptation night (Yes vs. No), and PSG scoring methods (Rechtschaffen and Kales (R&K) vs. American Academy Sleep Medicine (AASM)). Studies not performing PSG or containing no information on the outcomes of interest were excluded. Study design (S): Case-control studies were included. Case series, case reports, review papers, guidelines, editorials, comments, and statements were excluded. Other eligibility criteria required that the reviewed papers were published in peer-reviewed journals in English. If the same participants were used in more than one publication, then only the dataset with the most relevant information for our meta-analysis was used to avoid data duplication.

Information sources We searched MEDLINE via OVID (up to Nov 1st, 2019); EMBASE via OVID (up to Nov 1st, 2019); PsycIFNO via EBSCO (Up to Dec 29th, 2019); all EBM databases via OVID (up to Nov 1st, 2019); and CINAHL via EBSCO (up to Nov 1st, 2019).

Search Our search strategies for databases are available in Tables S1-S5. We also screened and checked the reference lists of all primary studies and review papers for additional references. We initially conducted the search strategies on May 17th, 2019. We repeated the same strategies on Nov 1st, 2019 - Dec 29th, 2019 to identify newly published studies and update the results.

Study selection 7

Two investigators (Ren, R. and Zhang, Y.) independently selected relevant publications according to the eligibility criteria. We discussed and consulted with the senior author (Tang, X. D.) for resolving any disagreement. When a study included more than one PD group of interest (e.g., early stage and middle stage PD patients), we considered it more than once to evaluate the different comparisons.

Data collection process Two investigators (Ren, R. and Zhang, Y.) independently extracted the data using a pre-designed form. Disagreements were resolved by thorough consultation and discussion with the senior author (Tang, X. D.). The extracted data were entered by one investigator (Zhang, Y.) and verified by two reviewers (Ren, R. and Zhang, Y.). Data were extracted from the original studies and by contacting the authors when necessary. The PSG variables examined in this review include TST, wake time after sleep onset (WASO), sleep efficiency (SE), and percentage of N1, N2, N3 and REM sleep, and REM latency. In AASM scoring rules, N3 represents slow wave sleep (SWS) and also replaces stage 3 and stage 4 in the R&K nomenclature [31]. Thus, the data for stage 3 and stage 4 in the included studies were also extracted for estimating SWS. Additional PSG variables include PLMI, apnea hypopnea index (AHI), arousal index (AI), data from power spectral analyses (PSA) (i.e., alpha, beta, delta, theta, and gamma frequency activity), as well as sleep spindles and REM/NREM stability and transitions. Demographic, clinical, and methodological variables extracted included the number of participants and their mean age, sex (male percentage), BMI, PD severity (UPDRS or MDS-UPDRS part III score), PD duration (years), H&Y stage, LEDD (mg/day), percentage benzodiazepine usage in PD patients, percentage antidepressant usage in PD patients, ESS score of PD patients, PSQI score of PD patients, PLMI of PD patients, depression symptoms (BDI score) of PD patients, MMSE score of PD patients, comorbid RBD (Yes vs. No), exclusion of OSA (Yes vs. No), adaptation night (Yes vs. No), and PSG scoring methods (R&K vs. AASM).

Quality assessment 8

The adapted version of the National Institute for Health and Care Excellence (NICE) checklist [32] was used by two investigators (Ren, R. and Zhang, Y.) to assess the risk of bias of the included studies. The NICE checklist assisted in reviewing studies for internal validity by methodically evaluating the selection of case-control studies, statistical methods, and confounding factors [32].

Statistical analysis To estimate the pooled effect-sizes (standardized mean difference (SMD)) for the PSG differences between PD patients and controls, the mean, standard deviation, and sample size for each group were entered for calculation. For the global effect-size estimate of each PSG variable, the I2 and Q statistic were calculated to test the presence and magnitude of heterogeneity, and to inform on the degree of overlap between the 95% confidence intervals (CIs) of different studies. The random effects model was applied to get a relative conservative finding in our meta-analysis. The Egger regression method [33] was used to examine publication bias, with p values of <0.05 suggesting the presence of bias. The Duval and Tweedie's trim and fill test was applied to adjust the effect sizes when publication bias was detected [34].

A meta-regression or subgroup analysis (depending on whether the potential moderators were continuous or categorical variables) was carried out to examine the potential factors that could moderate heterogeneity between studies exploring the PSG differences between PD patients and controls. The following pre-defined moderators were investigated: age, male percentage, BMI, PD severity (UPDRS or MDS-UPDRS part III score), PD duration (years), H&Y stage, LEDD (mg/d), percentage of benzodiazepine usage in PD patients, percentage of antidepressant usage in PD patients, mean ESS score of PD patients, mean PSQI score of PD patients, mean PLMI of PD patients, mean BDI score of PD patients, mean MMSE score of PD patients, percentage of RBD in PD patients, exclusion of OSA (Yes vs. No), adaptation night (Yes vs. No), and PSG scoring methods (R&K vs. AASM). In addition, we also performed a supplementary meta-analysis to explore PSG 9

differences between PD patients with and without RBD. All analysis in the present review was performed using Comprehensive Meta-Analysis software.

Results

Study selection Our search yielded 8167 publications (Fig. 1). After removing the duplicates, the title and abstract of the remaining 5292 articles were screened. A total of 400 studies were selected for full text review. Of these, 67 articles [18, 20, 21, 35-98] were found to meet inclusion criteria for the systematic review (Table 1), and 63 of the 67 studies were used in the meta-analysis. The excluded studies with reasons for their exclusion are listed in Table S6.

+++Figure 1. Flow chart used for the identification of eligible studies.+++

Description of the included studies Among the 67 studies (Table 1 and Tables S7-S8), 50 studies [18, 20, 21, 35-71, 86-95] explored PSG differences between PD patients and controls (four [86-89] of these 50 studies were only included in the systematic review, and not the meta-analysis). The sample sizes of the 50 studies ranged from 17 participants (8 patients with PD and 9 controls) [51] to 251 participants (158 patients with PD and 93 controls) [71]. Mean age of PD patients and controls ranged from 45.8 to 70.2 y (reported in 47 studies). Males as percentages of PD patients and controls ranged from 22.2-100% (reported in 46 studies). Mean BMI of PD patients and controls ranged from 22.1 to 32.46 kg/m2 (reported in 14 studies). Mean disease duration of PD patients ranged from 1.0 to 14.6 y (reported in 39 studies). Mean H&Y stage of PD patients ranged from 1.0 to 3.1 (reported in 35 studies). Mean LEDD of PD patients ranged from 0 to 1189 mg/day (reported in 32 studies). Mean ESS score of PD patients ranged from 2.4 to 12.3 (reported in 17 studies). Mean MMSE score of PD patients ranged from 25.58 to 29.3 (reported in 14 studies). 29 studies [18, 20, 21, 35, 10

36, 38, 45, 46, 49-53, 55-60, 62, 64, 65, 68, 71, 87, 89, 90, 92, 95] reported scores for motor symptoms evaluated using the UPDRS part III or MDS-UPDRS part III. Only one [36] of the 50 studies performed a home PSG. 23 studies [20, 42, 43, 48-54, 57, 59-62, 65, 71, 88-91, 93, 95] used AASM PSG criteria and most other studies used R&K criteria for scoring sleep (two studies [18, 70] did not report which scoring criteria was used). Seventeen studies [18, 20, 39, 41, 44-47, 50, 51, 56, 61, 63, 68, 69, 71, 86] included adaptation nights for PSG recordings, the other 33 studies performed only one night of PSG without adaptation. 33 studies [18, 21, 35, 37, 43, 45-47, 49-52, 54, 56-58, 60, 63-68, 70, 71, 86-92, 94] reported the rate (0-55.5%) of benzodiazepine usage and 35 studies [21, 35-37, 43, 45-47, 49-55, 57-60, 63-68, 70, 71, 86-92, 94] reported the rate (0-60%) of antidepressant usage. The quality assessments of these studies are available in Table S9.

Ten [43, 50, 57, 58, 60, 61, 65, 67, 90, 91] of the 50 studies described above concurrently included a PD with RBD group, a PD without RBD group, and a healthy control group. Seventeen additional studies [72-85, 96-98] included only PD with and without RBD groups. The characteristics of these 17 studies are also listed in Table 1 and Table S7. The quality assessments of these studies are available in Table S10.

Comparison between PD patients and controls: the whole sample In the whole sample, meta-analysis revealed significantly decreased TST (SMD =-0.499, 95%CI: -0.666 to -0.333), SE (SMD =-0.626, 95%CI: -0.813 to -0.439), N2 percentage (SMD =-0.290, 95%CI: -0.498 to -0.081), SWS percentage (SMD =-0.229, 95%CI: -0.427 to -0.030), REM percentage (SMD =-0.476, 95%CI: -0.647 to -0.304), REMD (SMD =-0.797, 95%CI: -1.153 to -0.080), and increased WASO (SMD =0.630, 95%CI: 0.331 to 0.929), N1 percentage (SMD =0.394, 95%CI: 0.209 to 0.579), REM latency (SMD =0.366, 95%CI: 0.254 to 0.477), AHI (SMD =0.206, 95%CI: 0.017 to 0.396), and PLMI (SMD =0.214, 95%CI: 0.026 to 0.402) in PD patients compared with controls (Table 2; p<0.05). There were no significant differences in SL and AI between PD patients and controls (p>0.05). A sensitivity analysis conducted by 11

removing the only home PSG study [36] resulted in the decrease in SWS in PD patients compared with controls only showing a trend toward a statistically significant difference (SMD =-0.191, 95%CI: -0.391 to 0.009, p=0.061), and did not change the direction of any other findings.

Publication bias was detected by Egger's test for the differences in SE and N1 between PD patients and controls (Figs. S1-S13). After adjusting for publication bias with the trim-and-fill method, the direction of effect sizes for these variables did not change, and the group differences for SE (SMD =-0.626, 95%CI: -0.813 to -0.439) and N1 (SMD =0.630, 95%CI: 0.439 to 0.822) remained significant (p<0.05).

Moderator analysis As given in Table S11, a meta-regression analysis revealed that an increased percentage of male PD patients across different studies was significantly associated with increased AHI in PD patients compared with controls (p=0.004). A subgroup analysis revealed that PD patients showed an increased AHI value compared with controls in studies with a percentage of male PD patients ≥ 65% (SMD =0.418, 95%CI: 0.242 to 0.593), but not in studies with a percentage of male PD patients < 65% (SMD =0.012, 95%CI: -0.299 to 0.323).

A meta-regression analysis also revealed that increased WASO, decreased SWS and REM percentage in PD patients compared with controls were significantly associated with increased PD duration across different studies (p<0.05). Decreased TST and SWS in PD patients compared with controls were significantly associated with decreased MMSE score in PD patients across different studies (p<0.01). Decreased SL in PD patients compared with controls were significantly associated with increased percentage of benzodiazepine usage in PD patients (p<0.05) across different studies, but was not associated with the percentage of antidepressant usage in PD patients (p>0.05). Increased WASO, N2 percentage, REML, and decreased TST were significantly associated with increased LEDD in PD patients across different 12

studies (p<0.05). Due to limited available data, we only explored the associations of PSQI score of PD patients with differences in TST, SE, SWS and REM sleep percentage between PD patients and healthy controls, and did not find any significant relationships (p>0.05).

A subgroup analysis revealed that PLMI of PD patients was a significant source of heterogeneity for differences in N1 and SWS percentage between PD patients and healthy controls (p<0.05). Significantly increased N1 (SMD =1.085, 95%CI: 0.462 to 1.709) and decreased SWS percentage (SMD =-0.730, 95%CI: -1.200 to -0.259) in PD patients compared with controls were only found in studies in which the mean PLMI of PD patients ≥12 events/h (p<0.05), but not in studies in which the mean PLMI of PD patients < 12 events/h (p>0.05). Increased N1 and decreased N2 percentage in PD patients compared with controls were significantly associated with an increased percentage of RBD in PD patients across different studies (p<0.05). A supplementary meta-analysis in studies exploring the differences in PSG variables between PD patients with and without RBD found that PD patients with RBD showed significantly decreased AI (SMD =-0.365, 95%CI: -0.717 to -0.013), increased REM percentage (SMD =0.307, 95%CI: 0.073 to 0.553) and PLMI (SMD =0.230, 95%CI: 0.118 to 0.341) compared to PD patients without RBD. There were no significant differences in other sleep parameters between PD patients with and without RBD (p>0.05) (Table 3). Egger's test did not reveal any publication bias for these findings (p>0.05) (Figs. S14-S25).

Methodologically, having an exclusion criterion to exclude OSA (Yes vs. No) was a significant source of heterogeneity for differences in SL and AI between PD patients and controls. PD patients showed increased SL (SMD =0.313, 95%CI: 0.070 to 0.556) compared with controls in studies not having an exclusion criteria for OSA, but not in studies having an exclusion criteria for OSA. Adaptation (Yes vs. No) was a significant source of heterogeneity for differences in REML between PD patients and controls (p=0.024). PD patients showed an apparent increase in REML 13

compared with controls in studies not having adaptation night (SMD =0.461, 95%CI: 0.323 to 0.598), but not in studies having an adaptation night. PSG scoring rules (AASM vs. R&K) was a source of heterogeneity for differences in SWS between PD patients and controls (p=0.005). PD patients showed a decreased SWS percentage compared with controls in studies using AASM scoring rules (SMD =-0.428, 95%CI: -0.667 to -0.189), but not in studies using R&K scoring rules (SMD =0.100, 95%CI: -0.185 to 0.386).

PSG findings which cannot be meta-analyzed Six studies explored PSA data for possible differences between PD patients and controls. Of these, two studies [45, 67] explored PSA in REM sleep, one study [86] explored PSA in NREM sleep, two studies explored PSA in REM and NREM sleep [51, 87], and one study [18] focused on slow wave activity between PD patients and controls. An additional three studies [21, 64, 88] assessed sleep spindle parameters, and one study [64] assessed K-complexes for potential differences between PD patients and controls. One additional study explored potential differences in REM/NREM stability and transitions [43]. We could not perform a meta-analytic evaluation for PSA and sleep spindles as the included studies were methodologically too different to be compared. The main findings of these studies are listed in Table 4.

Discussion

Summary of findings For the whole sample, we found that TST, SE, SWS, and REM were significantly decreased, and that WASO, REML, AHI, and PLMI were increased, in PD patients compared with controls. In our moderator analysis, we found that a) PD patients with RBD showed increased REM sleep percentage and PLMI, and decreased AI compared with PD patients without RBD, but no significant differences were obtained in any other sleep measures in comparisons of PD patients with and without RBD; b) decreased MMSE score in PD patients were associated with decreased TST and SWS 14

in PD patients compared with controls; c) antiparkinsonian drugs and benzodiazepines, but not antidepressants, and increased PLMI of PD patients, could impact differences in PSG variables between PD patients and controls; d) AHI was significantly increased in PD patients compared with controls in studies where the percentage of participants were male was above 65%, but not in studies where the percentage of male participants was below 65%. We also found that some clinical (i.e., PD duration) and methodological (i.e., adaptation night) factors were sources of heterogeneity between studies.

Sleep abnormalities in PD Previous studies using subjective evaluations have indicated that sleep disturbance is a common non-motor symptom in PD patients. The prevalence of sleep disturbances in PD patients evaluated by the PSQI is 63%, which is higher than that (45%) in other general medical conditions [99]. The subjective evaluation of sleep disturbances has not always been reliable in neurological diseases compared with objective sleep evaluations such as actigraphy and PSG [41, 100, 101]. Actigraphic data has revealed that PD patients had reduced light to dark ratio, later sleep onset time and decreased circadian rhythm amplitude compared with controls [20, 102]. PSG changes in our study focused on quantitative sleep parameters and further demonstrated disturbed objective sleep parameters (i.e. decreased SE, SWS, and REM sleep, increased WASO and REM latency) in PD patients. These changes demonstrate that PD patients have reduced sleep amount and poor quality sleep. These changes are important as decreased SWS and REM sleep may exacerbate neurodegeneration [25-27], and poor quality overnight sleep has been suggested as a risk factor for the pathological diagnosis of PD [103].

Sleep changes might be associated with circadian rhythm alterations in PD [20, 104]. Videnovic et al. reported diminished amount and reduced amplitude of melatonin secretion in PD patients compared with controls [104]. Breen et al. reported altered peripheral clock gene expression, a sustained increase in serum cortisol levels, 15

and decreased circulating melatonin level in PD patients compared with controls [20]. Additionally, other factors, such as genetics, neurodegeneration in sleep-wake regulatory systems, medication status, motor symptoms on sleep, and comorbid RBD were also associated with sleep disturbances in PD [105-109].

Moderators of PSG changes in PD Sex: Previous studies suggested that male and female PD patients show a different disease course and differences in non-motor and motor symptoms [110-112]. For non-motor symptoms, female PD patients were more likely to show fatigue, pain, trait anxiety, depression, restless legs, constipation, and higher disability compared with male PD patients [110, 111, 113-115]. Male PD patients were more likely to show obvious daytime sleepiness, sexual problems, olfaction, and cognitive impairments compared with females [115-117]. With respect to sleep disturbances, our findings suggested a significant sex effect on AHI in PD. Studies that included more male PD patients were more likely to find increased AHI in PD patients compared with controls, suggesting that male PD patients were more likely to show an increased AHI value compared with females. Differences in breathing control, upper airway anatomy, obesity, and hormones between males and females may play important roles in this sex difference [118].

Age: Advanced age has been demonstrated to be associated with decreased objective sleep quantity and poor sleep quality. A recent meta-analysis of 169 polysomnographic studies revealed that advanced age was significantly associated with decreased TST, SWS, and increased WASO, SL, N1, and AI in healthy adults [119]. In PD, age, as a basic demographic characteristic, is not only associated with increased prevalence of PD [120], but also could contribute to the disease phenotypes [121]. Previous studies suggested that age is a significant contributor of PD progression [121, 122], and that late-onset PD progresses faster than early-onset PD [122]. From a pathological perspective, the higher clinical progression rate in older subjects may be caused by faster degeneration of dopaminergic terminals [121, 123], 16

which may result in more rapid dysfunction in the sleep-wake system. It therefore could be speculated that PD patients with advanced age are more likely to show worsened sleep compared with younger patients. Furthermore, increased overall disease burdens (i.e., a larger number of comorbidities and medications) in older people and the effects of natural aging on sleep [121, 124, 125], might also contribute to worsened sleep in PD patients with advanced age. However, our findings did not reveal significant associations between decreased TST, SWS, and REM sleep, and increased N1 and WASO in PD patients compared with controls and advanced age of PD patients. This may be due to limited variations of mean age across studies included in our systematic review. In our included studies describing PSG changes in PD patients and controls, 47 studies reported that the mean age of PD patients ranged from 52.6 to 70.2 y, and 41 of 47 studies have the mean ages above 60 y.

PD duration: Disease duration is of clinical relevance and is related to several aspects of PD. For instance, longer PD duration is a significant predictor of disabling dyskinesia [126], and is also correlated with presence of freezing of gait in PD patients [127]. Jiménez et al. developed an equation including retinal nerve fiber layer thickness and PD duration as independent variables, which could predict patients’ UPDRS score [128]. With respect to sleep, our findings suggest that PD patients with longer PD duration were more likely to show changes in sleep macrostructure (i.e., increased WASO, and decreased SWS and REM sleep). One recent study also showed that longer duration was associated with changes in sleep microstructure (i.e., increased cyclic alternating pattern rate) in PD patients [129]. From a neuropathological perspective, PD-related neuroimaging changes, such as increased functional activity in the putamen, thalamus, globus pallidus, cerebellum, pons, and sensorimotor cortex, and decreased functional activity in the lateral premotor cortex and parieto-occipital association regions, continuously worsen over time [2, 130, 131]. Some of these brain regions are involved in sleep-wake regulation, and may partly explain the effects of disease duration on sleep in PD. 17

Effects of drugs: Clinically, the non-motor and motor symptoms of some PD patients did not allow the withdrawal of pharmacotherapy (i.e., antiparkinsonian drugs, benzodiazepines,

and

antidepressants)

in

study

subjects.

Withdrawal

of

pharmacotherapy is unethical and would have negative motor effects which could interact with studies focusing on sleep in PD [43]. It was therefore important to ask whether and how pharmacotherapy influences sleep measures in PD. The dopaminergic system plays important roles in sleep-wake regulation [11, 132, 133]; thus, antiparkinsonian therapy (i.e., levodopa and/or dopamine agonists) which impact dopaminergic system could therefore potentially influence sleep. Previous studies with small sample sizes have reported contradictory findings about the effects of antiparkinsonian therapy on sleep in PD. For instance, it has been suggested that levodopa may have a direct effect on sleep macrostructure or may improve sleep by improving nocturnal motor performance [18]. Ferreira et al. also reported that levodopa treatment increased SE, reduced SL and WASO in PD patients [54]. By comparison, Diederich et al. reported no association between levodopa usage and objective measurements of sleep parameters [134], and levodopa usage may even worsen subjective sleep quality in PD patients [135, 136]. Our meta-regression analysis including a relatively large sample size revealed that LEDD was a significant source of heterogeneity for PSG changes in PD, and that increased WASO and REM latency, and decreased TST were associated with increased LEDD in PD patients. These findings suggested that antiparkinsonian therapy is a significant contributor to alterations in objective sleep parameters in PD patients. Therefore, from a clinical perspective, objective sleep data should be closely monitored during the course of antiparkinsonian drug titration, especially for a higher dosage in PD patients.

Benzodiazepines and antidepressants are also commonly used for treating sleep disturbances and depression/anxiety symptoms in PD. This may potentially bias the pooled effect size for comparisons in PSG variables between PD patients and controls. Our findings suggested that antidepressant usage was not associated with significant changes in any of the PSG parameters we examined. By comparison, benzodiazepine 18

usage in PD only reduced sleep latency but did not impact sleep continuity and sleep architecture. Therefore, it could be speculated that the increased SL in PD could be masked by benzodiazepine effects, which may have been a factor in our not finding any significant difference in SL between PD patients and controls in the meta-analysis of the whole sample.

Presence of RBD: RBD, characterized by loss of muscle atonia during REM sleep and related to dream-enacted behaviors [137], has been suggested to show a high prevalence (42.3%) in PD patients [138]. A recent meta-analysis of longitudinal studies revealed that over 90% of RBD patients converted to a neurodegenerative disorder after a long-term follow-up period [22]. Previous studies suggested that the presence of RBD in PD is associated with poor subjective sleep quality, which may result from the possible motor symptoms and distressing dreams in RBD [139, 140]. When it comes to objective sleep parameters, although previous studies have explored differences in PSG variables between PD patients with and without RBD, the exact changes have not been established. Our meta-regression analyses suggest that RBD is not associated with alterations in sleep continuity and deep sleep in PD patients. This finding was verified by our supplementary meta-analysis which directly compared PSG variables between PD patients with and without RBD, indicating that presence of RBD only slightly increased REM sleep percentage (16.4 vs. 14.0%) and PLMI (16.5 vs. 12.3 events/h), but was not associated with decreased objective sleep quantity and sleep quality in PD patients. It therefore seems that the PSG changes in PD patients compared with controls found in our meta-analysis are not likely attributable to direct effects of RBD.

PLMI: Previous studies have reported that periodic limb movements in sleep (PLMS) are frequently observed in PD patients [141, 142]. Our meta-analysis revealed an increased PLMI in PD patients compared with healthy controls. In the general population, periodic limb movements have been suggested to be associated with a shallower and fragmented sleep, such as an increased amount of stage N1 and 19

decreased SWS [143, 144]. This is consistent with our findings that increased PLMI of PD patients is associated with increased N1 and decreased SWS percentage in PD patients compared with controls, suggesting that PLMS is a significant contributor to PSG measured sleep abnormalities in PD patients. The mechanisms underlying the effects of PLMS on sleep in PD are not fully understood. However, a reduction of striatal dopamine transporter binding in PD patients with PLMS compared with those without PLMS has been suggested [145]. Moreover, given the importance of the dopaminergic system in sleep-wake regulation [11, 132, 133], it can be speculated that sleep may be worse in PD patients with PLMS compared with those without PLMS. Thus, changes in the dopamine system could potentially contribute to the effects of increased PLMI of PD patients on sleep.

Cognitive impairment: Studies in the general population have shown that sleep disturbances are associated with the developments of cognitive impairment and dementia [146, 147]. In PD patients, sleep disruption can be found in the prodomal stage, while more severe symptoms of cognitive dysfunction/dementia are commonly presented in late stage PD [3]. A previous meta-analysis suggested that poor sleep has negative impacts on global cognitive function, long-term verbal recognition, long-term verbal recall, generativity, shifting, and fluid reasoning in PD patients [15]. Our meta-analysis work showed that PD patients with a lower MMSE score were more likely to have decreased TST and SWS, which provides further solid evidence in support of an association between sleep and cognitive impairment in PD patients, and also suggested that SWS and total sleep duration may be potential targets for improving cognitive impairments in PD patients. It should be noted that the Montreal Cognitive Assessment (MoCA), but not MMSE, was recommended by the Movement Disorders Task Force to detect cognitive impairment in PD [148]. Unfortunately, very few included studies used the MoCA, resulting in insufficient data to perform an analysis comparing methods.

Sleep spindles and quantification of electroencephalogram (EEG) frequency 20

components Sleep spindle are generated by a complex interaction between limbic, thalamic, and cortical regions [149], and alterations in functional and structural neuroimaging in these regions are sensitive to the development of neurodegenerative disease [2, 150]. These factors suggest that changes in sleep spindles may be a potential biomarker of neurodegenerative disease [150]. Christensen et al. suggested decreased sleep spindle density as a potential early biomarker of PD [88]. They also found morphological changes (i.e., a slower oscillation frequency, a longer duration, and higher maximum peak-to-peak amplitude) in sleep spindles in PD patients compared with controls [89]. These findings suggest that monitoring sleep spindles can be of clinical relevance for diagnosing PD. However, another study reported no difference in the quantity of sleep spindles between PD patients and healthy controls [64], therefore, we cannot make confident conclusions regarding their importance in PD. More studies performed using larger samples are needed in the future.

Sophisticated analyses such as PSA can be used to quantify changes in EEG frequency components and is useful for better defining the psychobiological profile of PD and is of clinical relevance. Emerging evidence suggests a significant association between changes in EEG frequency components and cognitive impairment in PD. For instance, Klassen reported that high theta band power is a predictive biomarker for dementia in PD [151], and Caviness et al. found that longitudinal changes in delta band power was associated with cognitive impairment in PD [152]. Additionally, Latreille et al. reported that higher absolute power in delta and theta bands during REM sleep, and lower baseline sigma power during NREM sleep was predictive of the development of dementia in PD [87].

Limitations Depression is a common non-motor symptom in PD [4]. A previous systematic review reported that the prevalence rates of major depressive disorder and minor depression is 17% and 22% in PD patients [153], respectively. The bidirectional 21

relationship between sleep disturbances and depression has been established [154]. However, only a few of our included studies reported on depression symptoms in PD patients. The version of tools used for assessing depression also varied across different studies. The limited data for the same version of individual tools (i.e., BDI or BDI-II) prevented assessing the effects of depression on the pooled effect sizes for PSG data in our meta-analysis. Similarly, there was insufficient data for performing a meta-analysis exploring the effects of anxiety on sleep in PD.

It is important to ask whether the severity of motor symptoms is related to sleep changes in PD patients. However, although over half of the studies making comparisons between PD patients and controls reported severity of motor symptoms (UPDRS part III or MDS-UPDRS part III score), it was unclear whether the motor symptoms were evaluated at the “on” or “off” condition in several studies. Five studies reporting UPDRS part III score at “off” condition [36, 57, 59, 62, 90], five reporting UPDRS part III score at “on” condition [21, 55, 57, 59, 90], and four reporting MDS-UPDRS part III score at “on” condition [18, 49, 52, 56]. There was therefore insufficient data to explore the association between sleep changes and motor symptoms in PD patients stratified by different version of tools (UPDRS and MDS-UPDRS) and different conditions (“on” and “off”).

Conclusions The present study conducted a comprehensive and in-depth exploration of the existing literature on PSG changes in PD compared with healthy controls. Although the included studies were methodologically diverse, they clearly identified objective sleep as a problem in PD patients. Our findings highlight the need to give greater considerations to PSG changes in PD care and in efforts to understand its etiology, neurobiology, and clinical outcomes. Interestingly, the presence of RBD was not associated with worsened sleep quantity and sleep quality in PD patients. Our findings also demonstrated that the associations between PD and sleep breathing events were moderated by sex, suggesting that male PD patients are more likely to show sleep 22

apnea events compared with female PD patients. Furthermore, short sleep duration and SWS deficits are indicative of impairments in cognitive function and are potential targets for therapeutic intervention aimed at improving the cognitive decline in PD. Our work also suggests that methodological considerations in future studies should include evaluating the contributions of potential heterogeneous factors (i.e., disease duration, medication status, sleep apnea) to PSG changes in PD.

Practice points 1) Patients with Parkinson’s disease exhibit disturbances in sleep continuity and sleep architecture compared with healthy controls. These sleep changes may not only result from pathology of the disease, but also may be associated with the existence of periodic limb movements in sleep and the use of antiparkinsonian drugs. 2) There are no obvious differences in sleep continuity and sleep architecture between male and female patients with Parkinson’s disease, but male patients are more likely to show sleep apnea events compared with female patients. 3) The presence of rapid eye movement sleep behavior disorder does not worsen sleep quantity and sleep quality in patients with Parkinson’s disease. 4) Decreased objective sleep duration and slow wave sleep are associated with cognitive impairment in patients with Parkinson’s disease.

Research agenda 1) Investigate the associations of the changes in sleep macrostructure and sleep microstructure

(i.e.,

cyclic

alternating

pattern),

quantification

of

electroencephalographic frequency components, and sleep spindles with changes in brain networks in patients with Parkinson’s disease. 2) Investigate whether treating sleep disturbances are associated with improvement of motor and other non-motor symptoms and its mechanisms in patients with Parkinson’s disease, such as whether alleviating deficits in sleep duration and slow wave sleep could reduce cognitive decline in patients with Parkinson’s disease. 23

3) Investigate whether sleep macrostructure and microstructure (i.e., cyclic alternating pattern), electroencephalographic frequency components, and sleep spindles in the general population could longitudinally predict the development of Parkinson’s disease, and whether these parameters could longitudinally predict therapeutic outcomes in patients with Parkinson’s disease. Conflicts of interest The authors do not have any conflicts of interest to disclose.

Acknowledgments This work was supported by the National Natural Science Foundation of China (81900087, 81530002, 81629002, 81800093).

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33

Table 1. Study characteristics (for more information please see Tables S7-S8). Study Sample size Percentage Mean of male Age Studies comparing PD patients with healthy controls Amato et al., 2018 [18] 7 PD (DNV) 9 PD (ADV) 11 PD (DYS) 7 controls Apps et al., 1985 [44] 12 PD 12 controls Arnaldi et al., 2016 [90] 10 PD+RBD 10 PD-RBD 10 controls Barut et al., 2015 [53] 21 PD 14 controls Bolitho et al., 2014 [55] 13 PD (unmedicated) 16 PD (medicated) 28 controls Breen et al., 2014 [20] 29 PD 15 controls Brunner et al., 2002 [86] 9 PD 10 controls Bušková et al., 2011 15 PD [63] 15 controls Cesari et al., 2018 [91] 29 PD+RBD 25 PD-RBD 27 controls Christensen et al., 2014 15 PD+RBD

Mean BMI

PD Duration (year)

H&Y stage

UPDRS-III or LEDD MDS-UPDRS (mg/day) -III score

2.30±0.60 7.05±1.55 9.98±1.73

1.14±0.14 2.11±0.11 2.18±0.12

15.28±2.17 20.00±3.33 13.18±3.34

6.08±4.4

2.5±0.8

NR

9.8 ± 3.7 7.3 ± 2.9

2.0 ± 0.8 1.9 ± 0.5

19.1 ± 6.2 13.5 ± 7.3

5.57±0.75

NR

19.23±1.97

1.0 ± 0.8 1.8 ± 1.3

2.0 ± 0.5 1.9 ± 0.5

27.0 ± 13.9 28.5 ± 14.4

NR

1.27±0.45

24±6

3.4±0.6

2.0±0.19

NR

NR

NR

NR NR NR NR 75% 75% 80% 70% 50% 61.9% 64.3% NR NR NR 53% NR 55.6% 40% 93.3%

52.6±3.13 61.6± 3.56 61.4±2.84 56.1±2.98 57.9±8.99 56.8±8.2 62.3 ± 7.9 53.3 ± 9.3 61.0 ± 7.0 61.9 ± 1.97 50.57 ± 2.30 64.8 ± 6.0 63.6 ± 9.8 68.3 ± 9.0 63±8 NR 65.3 ± 3.8 61.2 ± 2.2 59.8±10

NR NR NR NR NR NR NR NR NR 27.01±0.79 27.57±1.40 NR NR NR NR NR NR NR NR

93.3% 72% 68% 48% 73.3%

60.2±10 63.1±5.8 63.7±8.0 56.6±9.2 62.4 ± 5.2

NR NR NR NR 26.0 ± 3.2

NR NR NR NR

NR NR NR NR

Adaptation PSG scoring methods

57.6±20.2 495.0±56.4 846.6±96.4

NR

Yes Yes Yes Yes NR Yes Yes 773 ± 277 No 638 ± 289 No No 571.00±192.09 No No 0 No 420.3 ± 195.4 No No 312±157 Yes Yes 0 Yes Yes 0 Yes

NR NR NR NR R&K R&K AASM AASM AASM AASM AASM R&K R&K R&K AASM AASM R&K R&K R&K

NR NR NR NR

Yes No No No No

R&K AASM AASM AASM AASM

NR NR NR NR

[88] 15 PD-RBD 15 controls Christensen et al., 2015 15 PD; [89] 15 controls Christensen et al., 2016 19 PD+RBD [43] 8 PD-RBD 23 controls Diederich et al., 2013 33 PD [42] 37 controls Ehrminger et al., 2015 10 PD [52] 12 controls El-Senousy et al., 2012 24 PD [92] 10 controls Ferini-Strambi et al., 26 PD 1992 [69] 15 controls Ferreira et al., 2014 [54] 23 PD 31 controls Ferri et al., 2012 [61] 27 PD (16 PD+RBD PD-RBD) 19 controls Gagnon et al., 2004 [67] 7 PD+RBD 8 PD-RBD

and

53.3% 40% 46.7%

61.9 ± 6.1 58.3 ± 9.5 62.7±5.8

24.7 ± 2.2 23.2 ± 2.8 25.3 ±3.5

46.7% 68.4%

62.9±5.9 63.7±6.7

22.1±2.5 25.9±2.9

62.5% 30.4% 36.4%

68.8±8.4 56.7±9.2 65.5±11.6

24.1±3.6 23.1±2.5 25.3±3.9

56.8% 70%

66.7±9.0 60.5± 11.4

27±4 23.5±4.7

50% 62%

45.8±11.2 57.4±6.1

24±3.5 NR

60% 42.3%

54.6±8.76 64.7±12.0

25.1±2.24 27.1±2.1

46.7% NR NR 63%

63.1±9.2 NR NR 67.9±7.45

27.4±1.9 NR NR NR

36.8% 85.7% 37.5%

67.5±7.28 68.4±7.5 61.0±7.3

NR

NR

NR

NR 621.1±301.5

No No No

AASM AASM AASM

6.7±4.5

2.0±1.2

20.9±7.0

No No

AASM AASM AASM AASM AASM

4.6±3.3

NR

NR

NR

7.7±5.7

NR

NR

NR

1.9±1.3

2.0±0.5

NR

NR

No No No

823.9±354.1

No No

AASM AASM

NR

No No

AASM R&K

No Yes

R&K R&K R&K AASM AASM AASM

AASM R&K R&K

8.3±3.1

6±2.4

2.2±0.4

NR

14.7±6.3

20.7±7.5

1.8±1.1

2.03±0.7

NR

0

NR

NR

NR

0

4.36

1.8

NR

430.15

Yes No No Yes

NR NR

Yes No No

11

NR NR

5.4± 6.0 5.5 ±2.9

1.8 ±0.8 1.8±0.7

NR NR

15 controls Gaudreault et al., 2013 15 PD-RBD [58] 16 PD+RBD 16 controls González-Naranjo et al., 77 PD 2019 [93] 20 controls Happe et al., 2004 [64] 12 PD 10 controls Happe et al., 2005 [41] 17 PD 56 controls Imbach et al., 2016 [62] 64 PD 64 controls Latreille et al., 2015 [21] 50 patients who were dementia-free at baseline and follow-up time; 18 PD patients who were dementia-free at baseline but converted to dementia at follow-up time; 47 controls Latreille et al., 2016 [87] 50 patients who were dementia-free at baseline and follow-up time 18 PD patients who were dementia-free at baseline but converted to dementia at follow-up time 44 controls

60% 60%

65.5±6.3 63.1± 6.0

NR NR

69% 75% 79.2%

64.7± 8.0 NR 68.6± 7.1 NR 57.47±10.66 NR

60% 50% 50% 64.7% NR 38.2% 34.4% 66%

53.90±15.5 64.8±6.4 65.2±10.7 64.1± 6.2 NR 64.3±8.9 63.5±11.1 63.0±8.5

NR NR NR NR NR 25.3±4.4 25.1±3.8 NR

72%

70.2±7.6

64% 66%

5.4±3.8

2.2±0.8

20.7± 9.7

398.4±273.7

5.4±3.5

2.1±0.8

18.1±8.6

506.1± 383.3

6.00±4.40

2.42±0.50

NR

NR

No No

R&K R&K

No No No

R&K R&K AASM AASM R&K R&K R&K R&K AASM AASM R&K

6.25±4.49

2.25±0.62

17.75±13.61

NR

5.6±4.8

2.1±0.7

NR

NR

8.6±7.0

NR

21.9±8.4

426 ± 322

4.1±3.1

2.1±0.8

22.8±10.1

416.2±319.2

No No No Yes Yes No No No

NR

5.7±4.5

2.8±0.9

21.5±12.5

645.6±335.3

No

R&K

65.0± 10.6 63.0±8.5

NR NR

4.1±3.1

2.1± 0.8

22.8 ±10.1

416.2±319.2

No No

R&K R&K

72%

70.2± 7.6

NR

5.7 ±4.5

2.8±0.9

21.5±12.5

645.6±335.3

No

R&K

68%

64.8±10.9

NR

No

R&K

Margis et al., 2015 [51]

8 PD 9 Controls Maria et al., 2003 [40] 15 PD 15 controls Mariotti et al., 2015 [50] 49 PD+RBD 36 PD-RBD 30 controls Palma et al., 2013 [57] 33 PD (6 PD+RBD and 27 PD-RBD) 29 controls Placidi et al., 2008 [46] 12 PD 12 controls Pont-Sunyer et al., 2015 19 PD [49] 18 PD 14 controls Postuma et al., 2010 [66] 12 PD+RBD 26 controls Puligheddu et al., 2014 19 PD (no treatment) [39] 25 PD (treatment) 18 controls Schroeder et al., 2016 29 PD (early stage) [38] 21 PD (middle stage) 31 controls Shpirer et al., 2006 [37] 46 PD 30 controls Sixel-Döring et al., 2014 158 PD [71]

50% 40% 80% 80% 67.4% 50% 56.7% 75.8%

64.9 ± 6.1 64.8 ± 6.3 63±4 60±4 68.3 ± 7.05 69.0 ± 9.63 66.8 ± 6.98 62.92 ± 3.75

NR NR 27±1.3 27±1.2 NR NR NR 28.25± 3.95

3.0 ± 2.7

1.56

15.5 ± 5.6

6±5

NR

Unspecified

NR NR

NR NR

22.3±11.76 20.1±10.79

6.43±3.39

2 ± 0.5

10.35 ± 6.11

69% 50% 50% 57.9%

56.25±10.84 59.1±8.5 58.5±9.2 63.1± 11.2

32.46±10.11 NR 1.08±0.25 NR 26.4± 2.8 7.3± 3.7

50% 50% 83.3% 80.8% 57.9%

61.0 ± 11.2 50.8 ± 16.0 67.2±2.6 68.9±1.6 67.6 ±6.2

26.4 ± 3.7 24.4 ± 3.8 NR NR NR

68% 61% 58.6%

67.6±7.7 66.9±8.1 65.69±10.5

NR NR NR

81% 45.2% 50% 60% 66%

66.76±12.5 66.84±8.7 67.3±9.2 65.2±5.4 65 ± 10

NR NR NR NR NR

0

Yes Yes NR No No 435.1 ± 268.12 Yes 356.3 ± 243.93 Yes Yes 778.9±491.8 No

AASM AASM R&K R&K AASM AASM AASM AASM AASM R&K R&K AASM

1.37±0.4

14.6±5.8

0

1.9± 0.6

21.8± 10.8

550.2± 478.9

No Yes Yes No

7.8 ± 5.8

2.0 ± 0.7

14.4 ± 7.7

871.4 ± 669.2

No

AASM

NR

NR

NR

1.4±0.7

1.2±0.36

NR NR NR

No No Yes

R&K R&K R&K

4.6±3.9

1.9±0.7

NR

464.6±379.4

2.52±1.2

1.85±0.1

9.29±4.3

256.93±52

Yes Yes No

R&K R&K R&K

6.33±1.2

2.24±0.2

8.3±5.8

2.6±0.7

NR

NR

NR

1.8 ± 0.7

19 ± 10

NR

No No No No Yes

R&K R&K R&K R&K AASM

0

584±85.9

93 controls Sorensen et al., 2012 14 PD+RBD [60] 16 PD-RBD 17 controls Szakacs et al., 2016 [94] 4 PD+RBD 16 controls Terpening et al., 2013 40 PD [47] 20 controls Uludag et al., 2016 [95] 21 PD 18 controls van Gilst et al., 2015 18 PD (sleep benefit) [48] 20 PD (no sleep benefit) 20 Controls Wailke et al., 2011 [36] 16 PD (night with levodopa) 14 PD (night without levodopa) 16 controls Wetter et al., 2000 [68] 10 PD 10 controls Wetter et al., 2001 [45] 17 PD 10 controls Wienecke et al., 2012 10 advanced PD [59] 10 early PD 10 controls Yong et al., 2011 [35] 56 PD 68 controls

58% 78.6%

65 ± 7 63.4±6.7

NR 26.3±3.2

50% 47.1% NR NR 72.5%

62.0± 7.3 62.4±9.7 NR NR 63.6±7.6

25.7±5.1 23.4 ±3.1 NR NR NR

40% 61.9% 22.2% 33.3%

66.1±9.5 68.0 63.5 61.0 ± 5.9

NR NR

10.52±4.97

1.90±1.02

NR

NR

NR

7.7 ± 4.3

NR

NR

777 ± 384

70% 55% 44% 79%

63.2 ± 7.8 58.5 ± 7.5 61±8 62±9

NR NR NR NR

4.6 ± 2.7

NR

NR

542 ± 270

5.6±3.9 5.3±2.6

NR NR

21.31±8.0 26.36±9.6

667 ± 474 483 ± 285

44% 70% 50% 58.8% 60% 100%

58±6 65.2 ± 5.6 64.1 ± 5.9 65.9 ± 2.9 64.5 ± 2.2 63.8 ± 7.6

NR NR NR NR NR NR

50% 50% 60.7% 55.9%

58.7 ± 7.5 63.4 ± 8.0 65.4±9.1 59.3±9.1

NR NR 22.8±3.7 23.9±3.8

NR

1.6±0.8

23±11

623±360

NR

1.8± 0.8

23± 10

680± 552

NR

NR

NR

NR

4.1±4.4

1.7±0.5

NR

401.5±451.9

5.5±4.2

2.2±0.6

20.4±6.3

NR

3.4±0.6

1.8±0.2

16.9±1.1

0

14.6 ± 5.4

3.1 ± 0.7

28.7 ± 7.2

1189 ± 420

3.3 ± 1.3

1.0 ± 0.0

10.0 ± 2.4

0

6.4±4.1

2.5

21.5±11.8

409.4±265.1

Yes No

AASM AASM

No No No No Yes

AASM AASM R&K R&K R&K

Yes No No No

R&K AASM AASM AASM

No No No No

AASM AASM R&K R&K

No Yes Yes Yes Yes No

R&K R&K R&K R&K R&K AASM

No No No No

AASM AASM R&K R&K

Zhang et al., 2019 [70]

12 PD+RBD 23 controls Zhong et al., 2013 [56] 12 PD 11 controls Zoetmulder et al., 2016 10 PD+RBD [65] 10 PD-RBD 10 Controls Studies only included PD patients with and without RBD Arnulf et al., 2015 [75] 78 PD+RBD 36 PD-RBD Bargiotas et al., 2019 24 PD+RBD [96] 26 PD-RBD Benninger et al., 2010 13 PD+RBD [81] 13 PD-RBD Bugalho et al., 2018 [84] 18 PD+RBD 8 PD-RBD De Cock et al., 2007 41 PD+RBD [82] 10 PD-RBD Figorilli et al., 2017 [97] 37 PD+RBD 25 PD-RBD García-Lorenzo et al., 24 PD+RBD 2013 [79] 12 PD-RBD Gong et al., 2014 [77] 63 PD+RBD 49 PD-RBD Huang et al., 2018 [72] 61 PD+RBD

41.7% 43.5% 67% 55% 70%

68.83±2.98 63.39±2.14 62.2±8.9 66.6 ± 7.1 66.60 ± 3.44

60% 30%

NR NR NR NR NR

NR

NR

NR

NR

No No Yes Yes No

NR NR R&K R&K AASM

2.8±3.6

1.8 ±0.6

23.2±6.6

276 ±336

7.89±5.93

2.14±1.61

21.98±16.70

498 ± 252

66.80 ± 8.72 NR 56.20 ± 8.28 NR

5.00±5.08

1.89±0.63

26.00±11.61

324 ± 275

No No

AASM AASM

71.8% 66.7% NR

62.8 ± 6.8 56.8 ± 10.5 61.5±7.8

24.7 ± 3.3 24.8 ± 3.1 24.7±4.0

NR NR 11.9±3.9

NR NR 2.5±1.1

NR NR 38.5±20.1

618 ± 321 545 ± 336 1171.9±397.2

No No No

AASM AASM AASM

NR 71.4%

63.3±8.7 62.2±7.7

25.2±4.4 25.9 ± 4.9

11.7±4.4 10.7± 5.4

2.8±0.9 2.5±0.2

32.8±9.2 24.4±9.3

1241.6±853.8 805.2± 391.0

No No

AASM NR

91.8% 55.6% 75% NR

63.1±8.0 67.0±10.9 71.3±8.1 NR

25.1±5.4 NR NR NR

8.1± 4.6 NR NR NR

2.4± 0.3 2.2±0.7 2.8±0.8 NR

21.7± 8.4 NR NR NR

1012.1± 502.7 NR NR NR

No No No No

NR AASM AASM R&K

NR 64.9% 44% 66.7%

NR 66.0±7.5 62.7±10.1 62.4± 8.4

NR NR NR NR

NR 8.2±4.3 8.0±5.0 9.6±3.8

NR 2.2±0.5 2.1±0.6 NR

NR 18.1±11.1 16.2±9.5 17.5± 9.1

NR 796.2±486.0 704.4±421.9 NR

No No No No

R&K AASM AASM AASM

50% 63.49% 61.22% 72.1%

56.3±11.5 66.84 ± 7.80 65.30±11.03 64.7±5.9

NR 22.85±3.12 22.28±3.17 23.6±2.8

9.7±4.5 4.75 4.33 3.0(1.5–5.0)

NR 2.5 (1–4) 2.0 (1–3) 2.0(1.5–2.5)

12.6±7.0 22.50±9.83 18.26±9.45 22.36±1.29

NR 509.58±314.50 496.09±259.23 285.97 ± 20.60

No No No No

AASM AASM AASM AASM

53 PD-RBD 52.8% 62.8±9.3 23.5±3.0 2.0(1.0–5.5) 2.0(1.5–2.5) 24.71±1.42 310.67 ± 22.67 No AASM Kumru et al., 2004 [83] 6 PD+RBD 83.3% 48.8 NR 16.7 NR NR NR Yes R&K 4 PD-RBD 50% 54.75 NR 20.75 NR NR NR Yes R&K Louter et al., 2014 [85] 23 PD+RBD 73.9% 64.3±9.4 NR 9.5±6.4 2.2 NR 1089.4±582.9 No NR 22 PD-RBD 59.1% 58.1±8.8 NR 4.3±2.8 1.95 NR 697.7±563.1 No NR Neikrug et al., 2014 [76] 36 PD+RBD 69.4% 67.25±7.3 NR 6.9±6.4 1.85 19.4±7.5 828.9±618.9 No AASM 26 PD-RBD 69.2% 68.38± 10.0 NR 5.8±4.0 1.875 16.9±6.7 845.2±572.1 No AASM Plomhause et al., 2013 17 PD+RBD 47.1% 65 ± 8 NR 0.92 NR 14 ± 8 0 Yes R&K [78] 40 PD-RBD 67.5% 60 ± 12 NR 1.25 NR 15 ± 6 0 Yes R&K Sixel-Dӧring et al., 2011 210 PD+RBD 62% 69±8 NR 8.7±4.4 3.2±1.1 30±14 733± 357 No NR [80] 247 PD-RBD 64% 66±11 NR 7.3±5.6 2.9± 0.9 28±15 640±357 No NR Sixel-Dӧring et al., 2016 49 PD+RBD 63% 64±9 NR NR NR 16 ± 8 394 ± 221 No AASM [74] 42 PD-RBD 57% 63±12 NR NR NR 19 ± 12 362 ± 284 No AASM Sobreira-Neto et al., 55 PD+RBD 54.5% 60.4±10.6 25.0±4.0 8.88±4.83 1.99±0.4 17.8±11.4 889.24±426.8 No AASM 2019 [98] 11 PD-RBD 60% 58.4±16.1 25.3±3.1 8.84±7.12 1.81±0.4 16.2±10.4 490.91±260.3 No AASM Zhang et al., 2016 [73] 35 PD+RBD 85.7% 66.71±7.21 23.86±2.72 5.45±4.99 1.81±0.82 34.57±16.84 410.18±354.05 No R&K 11 PD-RBD 54.5% 62.46±4.80 22.45±2.90 5.59±3.28 1.41±0.66 28.18±10.65 268.82±199.84 No R&K AASM, American Academy of Sleep Medicine; ADV: advanced patients; BMI, body mass index; H&Y stage, Hoehn & Yahr stage; DNV: de novo patients; DYS: dyskinetic patients; LEDD, levodopa equivalent dose; NR, not reported; PD, Parkinson’s disease; RBD, REM sleep behavioral disorder; R&K, Rechtschaffen and Kales; MDS-UPDRS: Movement Disorder Society-sponsored revision of the Unified Parkinson Disease Rating Scale; UPDRS, Unified Parkinson Disease Rating Scale.

Table 2 Summary of meta-analysis between PD patients and controls No. of comparison No. of PD/C Means of PD Means of Controls SMD (95%CI) Q I2 TST min 52 1049/1085 350.938 383.520 -0.499(-0.666 to -0.333)* 163.059*** 68.723 SL min 36 918/796 21.721 18.665 0.067(-0.175 to 0.308) 184.268*** 81.006 WASO min 20 421 354 73.941 55.853 0.630(0.331 to 0.929)*** 71.431*** 73.401 SE % 52 1308/1204 74.570 81.528 -0.626(-0.813 to -0.439)*** 232.163*** 78.033 N1% 37 900/840 13.732 10.683 0.394(0.209 to 0.579)*** 117.025*** 69.237 N2% 38 933/877 47.600 49.978 -0.290(-0.498 to -0.081)** 160.176*** 76.900 SWS% 50 1118/1083 14.671 16.208 -0.229(-0.427 to -0.030)* 231.944*** 78.874 REM% 55 1330/1252 13.685 16.677 -0.476(-0.647 to -0.304)*** 218.790*** 75.319 REML min 29 714/661 127.143 102.513 0.366(0.254 to 0.477)*** 28.637 2.225 REMD 4 80/93 -0.797(-1.153 to -0.080)* 13.036** 76.986 AHI (events/h) 27 564/555 7.726 5.793 0.206(0.017 to 0.396)*** 58.117*** 55.262 AI (events/h) 22 429/444 12.307 13.994 -0.165(-0.465 to 0.134) 90.468*** 76.787 PLMI (events/h) 25 674/590 13.958 10.429 0.214(0.026 to 0.402)* 57.426*** 58.207 *p< 0.05, **p < 0.01, ***p < 0.001. %, percentage; Q, Cochran's Q statistic; AHI, apnea hypopnea index; AI, arousal index; PLMI, Periodic limb movement index; REM, rapid eye movement sleep, REMD, rapid eye movement sleep density; REML, rapid eye movement sleep latency; PD/C, Parkinson’s disease/controls; SWS, slow wave sleep; SE, sleep efficiency; SL, sleep latency; SMD, standardized mean difference; TST, total sleep time. Means for REMD were not calculated because definitions and algorithms of REMD varied across studies.

Table 3 Supplementary meta-analysis for exploring the PSG differences between PD patients with and without RBD No. of comparison No. of PD+RBD/PD-RBD Means of PD+RBD Means of PD-RBD SMD (95%CI) Q I2 TST min 19 538/377 351.170 337.149 0.158(-0.021 to 0.337) 27.631 34.856 SL min 16 740/605 24.573 19.623 0.177(-0.159 to 0.514) 109.195*** 86.263 WASO min 3 141/72 91.275 83.972 0.132(-0.162 to 0.425) 1.137 0 SE % 23 872/699 73.704 72.169 0.006(-0.190 to 0.202) 61.958*** 64.492 N1% 20 631/403 13.311 12.447 0.033(-0.179 to 0.245) 46.015*** 58.709 N2% 19 607/377 49.840 50.628 -0.246(-0.600 to 0.107) 113.045*** 84.077 SWS% 24 913/759 15.187 15.171 0.106(-0.177 to 0.389) 149.165*** 84.581 REM% 27 966/803 16.406 14.023 0.307(0.073 to 0.533)* 116.147*** 77.615 REMD 0 REML min 11 529/425 143.271 137.995 -0.016(-0.148 to 0.116) 2.236 0 AHI (events/h) 17 520/390 7.422 5.821 -0.002(-0.158 to 0.154) 19.187 16.612 AI (events/h) 9 265/124 12.529 14.674 -0.365(-0.717 to -0.013)* 17.195* 53.475 PLMI (events/h) 18 739/607 16.464 12.341 0.230(0.118 to 0.341)*** 16.385 0 *p< 0.05, **p < 0.01, ***p < 0.001. %, percentage; Q, Cochran's Q statistic; AHI, apnea hypopnea index; AI, arousal index; PLMI, periodic limb movement index; REM, rapid eye movement sleep, REMD, rapid eye movement sleep density; REML, rapid eye movement sleep latency; RBD, REM sleep behavior disorder; PD, Parkinson’s disease; PSG, polysomnography; SWS, slow wave sleep; SE, sleep efficiency; SL, sleep latency; SMD, standardized mean difference; TST, total sleep time; WASO, wake time after sleep onset;. Means for REMD were not calculated because definitions and algorithms of REMD varied across studies.

Table 4. Summary of findings that were not subjected to meta-analysis Study Outcomes Sample size Main findings Amato et PSA focusing on 7 de novo PD patients; In early sleep, control subjects showed a significantly greater amount of slow wave activity, diffused over the whole al., 2018 slow wave 9 advanced PD patients; scalp, compared with PD patients, as a whole group and separately. Moreover, delta power was greater in DNV [18] activity 11 dyskinetic PD patients; compared with the other patients’ groups and in ADV compared with DYS, with the DYS group having the lowest 7 controls content of slow wave activity. In late sleep, when comparing between groups selecting only frontocentral channels, delta power was lower in DNV patients compared with the other groups and in ADV patients compared with DYS patients, with the DYS group having the greatest content of slow wave activity among PD patients. Brunner et PSA in NREM 9 PD; For the EEG power spectrum of NREM sleep, de novo PD patients showed a significant decrease in the low-delta al., 2002 sleep 10 controls frequency range (0.78-1.2 Hz), but no significant difference in the spectral profile within the high-δ, α, β, and Ɵ range compared with controls; there was an increase in the sigma frequency range in de novo PD patients compared with [86] controls, but it did not reach a statistically significant level. Christensen Sleep spindle 15 PD+RBD; PD patients with RBD had a significantly lower mean sleep spindle density than the control group in N2, N3 and all et al., 2014 15 PD-RBD; NREM combined. The PD patients without RBD had a significantly lower mean sleep spindle density than the control [88] 15 controls group in N2 and all NREM combined. Christensen Sleep spindle 15 PD; PD patients had a decreased sleep spindle density, and the sleep spindles have a longer duration, a slower oscillation et al., 2015 15 controls frequency and higher maximum peak-to-peak amplitude compared with controls. [89] Christensen REM/NREM 19 PD+RBD; PD patients showed significantly lower REM stability than controls; PD patients with RBD showed significantly lower et al., 2016 stability and 8 PD-RBD; NREM stability and more REM/NREM transitions than controls. [43] transitions 23 controls Gagnon et PSA in REM 7 PD+RBD; 8 PD-RBD; 15 There were no significant differences in any frequency band during REM sleep between groups. al., 2004 sleep controls [67] Happe et al., Sleep spindle 12 PD; 10 controls There was no difference between the quantity of sleep spindles, K-complexes, and K-alpha-complexes in PD patients 2004 [64] and compared with healthy controls. K-complexes Latreille et Sleep spindle 18 PD patients who were Sleep spindle density and amplitude were lower in PD patients who converted to dementia compared with both patients al., 2015 and slow wave dementia-free at baseline but who remained dementia-free and controls, mostly in posterior cortical regions. Dementia-free PD patients were [21] converted to dementia at intermediate between dementia patients and controls, with lower baseline sleep spindle density in all cortical areas

Latreille et al., 2016 [87]

follow-up time; 50 patients who were dementia-free at baseline and follow-up time; 47 controls PSA in NREM 18 PD patients who were and REM sleep dementia-free at baseline but converted to dementia at follow-up time; 50 patients who were dementia-free at baseline and follow-up time; 44 controls PSA in NREM 8 PD; 9 controls and REM sleep

compared with controls. PD patients showed lower slow wave amplitude compared with controls, but there was no significant difference between PD patients who developed or did not develop dementia

In REM sleep, PD patients who later developed dementia showed, at baseline, higher absolute power in delta and theta bands and a higher slowing ratio, especially in temporal, parietal, and occipital regions, compared to patients who remained dementia-free and controls. In NREM sleep, lower baseline sigma power in parietal cortical regions also predicted development of dementia.

Margis et Differences between patients and controls were most prominent in NREM sleep. Significantly increased NREM sleep al., 2015 alpha activity was found in left and right central, left and right frontal, left occipital and left parietal areas. Increased [51] sigma activity was found in right frontal, left central, left and right parietal and left occipital areas. Wetter et al., PSA in REM 17 PD; 10 controls The REM sleep EEG power spectrum of PD patients was characterized by a significant increase in the high-theta/alpha 2001 [45] sleep frequency range (6.3-10.9 Hz) compared with controls. ADV: advanced patients; DNV: de novo patients; DYS: dyskinetic patients; EEG, electroencephalogram; NREM, non rapid eye movement; PSA; power spectral analysis; PD, Parkinson’s disease; RBD, REM sleep behavioral disorder; REM, rapid eye movement.