Association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease

Association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease Si-Chun Gu, Xiao-Lei Yuan, Ping Yin, Yuan-Yuan Li, Chang-De Wang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3761895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson’s disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients. Methods In this cross-sectional study, a total of 1115 PD participants were enrolled from Parkinson's Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed at baseline. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models. Results We identified 426 (38.2%) RBD at baseline. The proportion of underweight, normal, overweight and obese at baseline was 2.61%, 36.59%, 40.36% and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m 2 , 28.10 kg/m 2 , and 28.23 kg/m 2 derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results. Conclusion The current study found a significant dose-response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients. Biological sciences/Neuroscience/Diseases of the nervous system Biological sciences/Neuroscience/Neural ageing Health sciences/Diseases/Neurological disorders/Movement disorders/Parkinsons disease body mass index rapid eye movement sleep behavior disorder Parkinson’s Disease Parkinson's Progression Markers Initiative restricted cubic spline Figures Figure 1 Figure 2 Figure 3 1. Introduction Body mass index (BMI) is a widely used measure of relative body weight and is considered the gold standard of general nutritional status 1 . Both high and low BMI are directly related to health risks including hypertension, type 2 diabetes, cardiovascular disease, and certain cancers, and can result in detrimental health outcomes. A change in BMI is considered an important outcome in monitoring health and well-being 2,3 . BMI has been found to be associated with function in several neurodegenerative diseases. In patients with amyotrophic lateral sclerosis, a BMI < 18.5 kg/m 2 is associated with reduced survival, while a BMI of 30–35 kg/m 2 is associated with increased survival 4 . In patients with dementia, a low BMI is associated with reduced survival and serves as an independent predictor of mortality, regardless of cognitive impairment severity 5 . Parkinson’s disease (PD) is a neurological degenerative disease characterized by motor symptoms including rigidity, tremor, bradykinesia, and postural instability, along with a wide range of non-motor symptoms, including sleep disturbances, autonomic dysfunction, neuropsychiatric disorders, and cognitive impairment 6 . The combination of the heterogeneous symptomatology mentioned above directly threatens the ability of individuals with PD to live independently and imposes a significant economic and global burden 7,8 . Given the high prevalence of weight variation in PD, increasing attention is being paid to investigate the role of BMI in PD. For the diagnosis of PD, several studies have associated obesity with a higher risk of developing PD. Conversely, patients with PD are consistently reported to have lower body weight compared to healthy controls 9 . When considering survival in PD, there is a significant inverse dose-response relationship between baseline BMI and mortality. BMI > 23 kg/m 2 contributes to extended survival rates, while BMI < 18.5 kg/m 2 is linked with poor survival 10 . The association between BMI and motor symptoms of PD has been explored, that is decreasing-BMI is associated with worse scores over time in UPDRS motor scores, whereas increasing-BMI is associated with better UPDRS motor scores 11 . Although the biological mechanisms have not been identified, potential contributors may include perturbation of hypothalamic metabolic regulation, gastrointestinal dysfunction, and alteration of energy expenditure and food intake 12 . However, to the best of our knowledge, the effects of BMI on PD non-motor symptoms have not been previously studied. As the most common disabling non-motor symptom of PD, the prevalence of rapid eye-movement (REM) sleep-related behavioral disorder (RBD) is around 20–50% in PD patients 13 . RBD is characterized by a loss of muscle atonia during REM sleep, leading to dream enactment behaviors that are frequently injurious to patients and their partners 14,15 . Previous studies have clearly observed U-shaped and inverse U-shaped relationships between sleep duration and BMI 16 . However, the impact of BMI on RBD in PD has not been fully clarified, and understanding is crucial for improving outcomes related to non-motor symptoms in patients with PD. Therefore, the objective of this study was to investigate the association between baseline BMI and RBD in patients with PD using a large population-based dataset. 2. Methods 2.1. Study design Study data used in the present study were obtained from the Parkinson's Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/data ). PPMI is an ongoing observational, international, multicentre cohort study aiming to identify blood-based, genetic, spinal fluid, and imaging biomarkers of Parkinson’s disease (PD) progression with longitudinal follow-up in a large cohort. The aims and methodology of PPMI study have been published elsewhere 17 . Study protocol and manuals and are available online. The study was approved by the Institutional Review Board at each site, and all participants provided written informed consent. For this study, we utilized the baseline dataset of PPMI from 33 participating outpatient PD treatment centers worldwide on the basis of inclusion and exclusion criteria previously published. All the methods were performed in accordance with relevant institutional guidelines and regulations. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guideline was included in supplementary material 1. Figure 1 illustrated the selection process of our study. Participants who were diagnosed as idiopathic PD, with baseline Rapid eye movement (REM) sleep behavior disorder questionnaire (RBDSQ) score and BMI data were included as study population. Healthy controls, scans without evidence of dopaminergic deficit (SWEDD) patients, prodromal patients, duplicated participants, individuals with missing baseline RBDSQ score or BMI data were excluded from this analysis. In total, 1115 patients with complete information, enrolled between November 2010 and June 2023, were included in our analyses. The data were downloaded on September 15, 2023. 2.2. Exposure and outcome Exposure was assigned as baseline BMI. Anthropometric data, including height and weight, were obtained from baseline data. BMI was calculated as weight divided by height squared (weight(kg)/height(m 2 )), and then classified into 4 WHO categories, including underweight (BMI < 18.5 kg/m 2 ), normal weight (18.5 ≤ BMI < 25.0 kg/m 2 ), overweight (25 ≤ BMI < 30 kg/m 2 ) and obese (BMI ≥ 30 kg/m 2 ) categories 18,19 . Outcome was assigned as REM-sleep behavior disorder (RBD) at baseline. The 10-item RBDSQ has been validated in PD patients and demonstrates good accuracy in identifying RBD. Items 1 to 4 of the RBDSQ assess the frequency and content of dreams, as well as their relationship to nocturnal movements and behavior. Item 5 asks about self-injuries and injuries to the bed partner. Item 6 consists of four subitems specifically assessing nocturnal motor behavior, including questions about nocturnal vocalization, sudden limb movements, complex movements, or items falling from the bed. Items 7 and 8 inquire about nocturnal awakenings. Item 9 focuses on general sleep disturbances, while item 10 pertains to the presence of any neurological disorder. The maximum total score on the RBDSQ is 13 points. Following the definition set by the International Parkinson and Movement Disorders Society (MDS) Task Force, we defined RBD as a baseline RBDSQ score equal to or greater than 5 20 . 2.3. Covariates To assess the potential influence of confounding factors, several important covariates were selected as a prior based on the literature. These covariates included age, sex, PD duration, depression (measured by the 15-item Geriatric Depression Scale [GDS-15] score in PPMI), levodopa equivalent daily dose (LEDD), hypertension (defined as self-reported hypertension, systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or use of antihypertensive drugs), and MDS Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) score at baseline 21 . All participants in the PPMI study underwent the standard test battery of assessments. In addition to the covariates mentioned above, sociodemographic characteristics and clinical battery relevant to this study including education, Hoehn & Yahr stage, serum uric acid, autonomic function assessed by the Scale for Outcomes for Parkinson’s Disease-autonomic function (SCOPA-AUT) score, anxiety assessed by State-Trait Anxiety Inventory (STAI) score, and daily living quality assessed by Modified Schwab and England Activities of Daily Living Scale (MSEADL) were adjusted as potential confounders in the models. Although PPMI collected an array of cerebrospinal fluid (CSF) biomarkers, these measures were only available for a small subset of participants and thus were not included in this study. 2.4. Statistical analysis Summary statistics were performed and tested for normality (Shapiro-Wilk). Continuous data were presented as median (interquartile range [IQR]) or mean ± standard deviation (mean ± SD), with categorical data presented as proportion and number (N [%]) as appropriate. Group comparisons were analyzed with use of Student’s t tests or Wilcoxon’s rank-sum tests for continuous data and Chi-square tests or Fisher’s exact tests for categorical data. Data were more than 99% complete. The remaining missing values were imputed by multivariable chained imputation with fully conditional specification, and imputed and reported results were similar 22 . All statistical tests were two-sided and the level of significance was set at 0.05. Participants were divided into two groups based on whether they had RBD. The relationship between BMI and RBD was examined using logistic regression models. The adjustment was accomplished via 3 models: ( 1 ) model 1, without any covariate adjustment; ( 2 ) model 2, adjusted for age, sex, PD duration, GDS-15 score, LEDD, hypertension, depression, and MDS-UPDRS I, II and III scores; ( 3 ) model 3, additionally adjusted for education level, serum uric acid, Hoehn & Yahr stage, STAI, SCOPA-AUT, and MSEADL scores as covariates. The results were presented as odds ratios (OR) with corresponding 95% confidence intervals (CI). Restricted cubic spline (RCS) analysis was also performed to examine the association of between baseline BMI and RBD based on univariable and multivariable adjusted logistic regression models 23 . To balance best fit and overfitting in the main splines, the number of knots, between three and seven, was chosen as the lowest value for the Akaike information criterion. If the difference in the number of knots was within two for different models, the lowest number of knots was selected 24 . The same number of knots from the main splines was also applied for stratified analyses to allow direct comparison of overall and stratified analyses, including test of interaction. We tested for potential non-linearity by using a likelihood ratio test comparing the model with only a linear term against the model with linear and cubic spline terms. Piecewise-linear models were then fitted to quantify the association between BMI and RBD. If evidence of non-linearity was found, a two-line piecewise linear model with a single change point was estimated by trying all possible values for the change point and selecting the value with the highest likelihood among those considered, while accounting for covariates. We fitted interactions to investigate effect modification by depression (with depression, without depression, GDS-15 ≥ 5 or not), motor subtype (tremor dominate [TD], non-TD including postural instability/gait disorder [PIGD] or Indeterminate), sex (male, female), and hypertension (yes, no) 25–27 . Due to the nonlinear association between BMI and RBD in the whole participants, we used continuous BMI and the quadratic term BMI 2 in multivariable adjusted logistic regression models (model 3) to allow for the nonlinearity during the interaction analyses. The first model to test for the depression-by-BMI interaction allowed for interaction with both the linear and quadratic terms of BMI 28 . Model A: RBD = BMI + BMI 2 + depression + BMI × depression + BMI 2 × depression + other covariates In the absence of interaction with the quadratic term, the model was then simplified to only allow for interaction with the linear term. Model B: CVD = BMI + BMI 2 + subtype + BMI × depression + other covariates The significance of the interaction was determined based on the highest level interaction term in the models, and here, lack of interaction was inferred when neither BMI 2 × depression (Model A) nor BMI × depression (Model B) were significant at the 5% level. Interactions by subtype, hypertension, and sex were examined in the corresponding manner, replacing “depression” above with hypertension, sex or subtype as relevant. To assess the robustness of the results, we additionally applied three sensitivity analyses. First, we examined the shape of BMI-RBD relation after excluding individuals who had a baseline Montreal Cognitive Assessment (MOCA) score less than 26 as the definition of cognitive impairment 29 . Second, we restricted the analysis to individuals in the first and second categories of education. Third, we performed the analysis after excluding other ethnic groups. All data were analyzed using R (version 4.0.2). 3. Results Of 1115 participants included in the study, we identified 426 (38.2%) RBD at baseline. Table 1 summarized the baseline characteristics of participants according to BMI categories. The proportion of underweight, normal, overweight and obese at baseline was 2.61%, 36.59%, 40.36% and 20.44%, respectively. The median BMI was 26.1, and the median age was 63.6 years. Within 4 BMI categories, significant differences were observed regarding the proportion of RBD, RBDSQ score, sex, education status, MOCA score, GDS-15 score, SCOPA-AUT score, proportion of hypertension, and serum uric acid. The individuals with higher BMI tended to have higher RBDSQ score, as well as a higher proportion of RBD. The proportion of RBD decreased with higher BMI. The proportion of male decreased with higher BMI. The individuals with lower BMI tended to have a lower proportion of the first category of education (< 13 years) and a higher proportion of the second category of education (13–23 years). Underweight individuals had higher MOCA score, GDS-15 score, and SCOPA-AUT score than others. Serum uric acid increased with higher BMI, with the median of 4.1 mg/dL in underweight individuals, 4.5 mg/dL in normal weight individuals, 5.4 mg/dL in overweight individuals, and 5.5 mg/dL in obese individuals. Moreover, a positive association was observed between hypertension and BMI. 17.2% of underweight individuals had hypertension, whereas 49.6% of obese individuals had hypertension. Table 1 Baseline characteristics of study population according to BMI measure by WHO BMI category Overall N = 1115 Underweight (< 18·5kg/m²) N = 29 Normal weight (18·5–24·9kg/²) N = 408 Overweight (25·0–29·9kg/m²) N = 450 Obese (≥ 30·0kg/m²) N = 228 P BMI 26.1 [23.9–29.4] 17.9 [16.9–18.4] 23.3 [21.7–24.2] 27.3 [26.0–28.7] 32.2 [30.9–34.6] < 0.001 Age (years) 63.6 [56.2–69.6] 61.0 [51.5–69.5] 63.2 [54.7–69.3] 64.2 [57.1–70.2] 64.2 [56.8–69.0] 0.207 Sex < 0.001 Male 435 (39.0%) 19 (65.5%) 201 (49.3%) 130 (28.9%) 85 (37.3%) Female 680 (61.0%) 10 (34.5%) 207 (50.7%) 320 (71.1%) 143 (62.7%) Education (years) 16.0 [14.0–18.0] 16.0 [15.0–18.0] 16.0 [14.0–18.0] 16.0 [14.0–18.0] 16.0 [13.0–18.0] 0.01 Education categories 0.05 23 years 19 (1.7%) 0 (0.0%) 9 (2.2%) 7 (1.6%) 3 (1.3%) Race 0.379 White 1043 (93.5%) 28 (96.6%) 385 (94.4%) 415 (92.2%) 215 (94.3%) Black 17 (1.5%) 0 (0.0%) 3 (0.7%) 11 (2.4%) 3 (1.3%) Asian 14 (1.3%) 1 (3.4%) 7 (1.7%) 5 (1.1%) 1 (0.4%) Other 41 (3.7%) 0 (0.0%) 13 (3.2%) 19 (4.2%) 9 (3.9%) PD duration (months) 7.8 [3.7–20.6] 8.7 [3.5–14.8] 8.6 [4.2–22.5] 7.1 [3.5–19.1] 8.2 [4.2–22.4] 0.156 LEDD 141.8 ± 317.0 155.1 ± 366.4 169.1 ± 335.6 122.4 ± 292.4 147.0 ± 328.3 0.317 Hoehn & Yahr Stage 0.871 Stage1 377 (33.8%) 10 (34.5%) 139 (34.1%) 152 (33.8%) 76 (33.3%) Stage2 713 (63.9%) 18 (62.1%) 261 (64.0%) 290 (64.4%) 144 (63.2%) Stage3 25 (2.2%) 1 (3.4%) 8 (2.0%) 8 (1.8%) 8 (3.5%) Motor subtype 0.178 TD 742 (66.5%) 17 (58.6%) 266 (65.2%) 294 (65.3%) 165 (72.4%) non-TD (PIGD or Indeterminate) 373 (33.5%) 12 (41.4%) 142 (34.8%) 156 (34.7%) 63 (27.6%) MDS-UPDRS I score 6.0 [3.0–9.0] 7.0 [3.0–11.0] 5.0 [3.0–9.0] 6.0 [3.0–9.0] 6.0 [3.0–10.0] 0.119 MDS-UPDRS II score 6.0 [3.0–9.0] 7.0 [3.0–11.0] 6.0 [3.0–9.0] 5.0 [2.0–9.0] 5.5 [3.0–10.0] 0.277 MDS-UPDRS III score 20.0 [14.0–27.5] 21.0 [15.0–29.0] 19.5 [14.0–26.0] 20.0 [14.0–27.0] 21.0 [15.0–29.5] 0.228 Total MDS-UPDRS score 33.0 [23.0–43.0] 32.0 [25.0–50.0] 32.5 [23.0–43.0] 32.0 [23.0–42.0] 35.0 [24.0–47.0] 0.085 RBDSQ score 4.0 [2.0–6.0] 3.0 [2.0–6.0] 3.0 [2.0–6.0] 4.0 [2.0–6.0] 4.0 [3.0–7.0] < 0.001 With RBD 0.012 No 689 (61.8%) 17 (58.6%) 270 (66.2%) 281 (62.4%) 121 (53.1%) Yes 426 (38.2%) 12 (41.4%) 138 (33.8%) 169 (37.6%) 107 (46.9%) MOCA score 26.7 ± 2.7 27.8 ± 2.3 26.5 ± 3.0 26.5 ± 2.8 26.9 ± 2.5 0.005 MSEADL score 92.5 ± 8.0 92.2 ± 7.1 91.8 ± 7.6 93.1 ± 7.6 92.3 ± 8.5 0.224 GDS-15 score 2.0 [1.0–3.5] 2.0 [2.0–5.0] 2.0 [0.0–3.0] 2.0 [0.0–3.0] 2.0 [1.0–4.0] 0.005 STAI score 62.0 [50.0–77.0] 77.0 [58.0–91.0] 62.0 [51.0–77.5] 61.5 [49.0–76.0] 60.0 [49.0–78.0] 0.224 SCOPA-AUT score 9.0 [6.0–14.0] 14.0 [6.0–18.0] 10.0 [6.0–14.0] 9.0 [5.0–14.0] 9.0 [6.0–14.0] 0.005 With hypertension < 0.001 No 655 (58.7%) 24 (82.8%) 264 (64.7%) 252 (56.0%) 115 (50.4%) Yes 460 (41.3%) 5 (17.2%) 144 (35.3%) 198 (44.0%) 113 (49.6%) Serum Uric Acid (mg/dL) 5.0 [4.2–5.9] 4.1 [3.4–5.4] 4.5 [3.9–5.3] 5.4 [4.5–6.1] 5.5 [4.5–6.4] < 0.001 Data were presented as N (%), mean ± SD, or median [IQR]. Abbreviations: BMI, body mass index; PD, Parkinson’s disease; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson’s Disease Rating Scale; RBDSQ, rapid eye movement sleep behavior disorder screening questionnaire; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson’s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living; MOCA, Montreal Cognitive Assessment; N, number; IQR, interquartile range; SD, standard deviation. Table 2 showed the association of BMI and RBD. In the crude logistic regression model, obese individuals had an OR of 1.73 (95% CI 1.24 to 2.41) for RBD compared with normal weight individuals, which indicated that the risk of RBD was increasing for rise in BMI. The strong positive association between BMI and RBD consistently existed in adjusted models. Compared with those of normal weight, obese individuals had ORs of 1.63 (95% CI 1.14 to 2.34) and 1.77 (95% CI 1.21 to 2.59) for RBD in model 2 and model 3. Table 2 The association between baseline BMI and the incidence of RBD in PD Model BMI Category Events/Participants RBD OR (95%CI) P value Model 1 a Obese 107/228 1.73 (1.24–2.41) 0.001 Overweight 169/450 1.17 (0.89–1.56) 0.25 Underweight 12/29 1.38 (0.63–2.95) 0.41 Normal weight 138/408 Reference – Model 2 b Obese 107/228 1.63 (1.14–2.34) 0.008 Overweight 169/450 1.14 (0.84–1.55) 0.39 Underweight 12/29 1.23 (0.52–2.83) 0.62 Normal weight 138/408 Reference – Model 3 c Obese 107/228 1.77 (1.21–2.59) 0.003 Overweight 169/450 1.17 (0.85–1.60) 0.34 Underweight 12/29 1.16 (0.48–2.70) 0.74 Normal weight 138/408 Reference – a: No covariate was adjusted. b: Age, sex, PD duration, GDS-15 score, LEDD, hypertension, and MDS-UPDRS I, II and III scores were adjusted. c: Education level, serum uric acid, Hoehn & Yahr stage, motor subtype, STAI, SCOPA-AUT, and MSEADL scores were additionally adjusted. Abbreviations: BMI, body mass index; PD, Parkinson’s disease; OR: odds ratio; CI: confidence interval; RBD, rapid eye movement sleep behavior disorder; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson’s Disease Rating Scale; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson’s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living. We used RCS to flexibly model and visualize the relation of BMI with RBD in PD with three knots (Fig. 2 A-C). For the risk of RBD, there was evidence of non-linearity with two main patterns seen: we found positive associations below the change points of BMI with the rapid increase of the risk of RBD, whereas there was little evidence of association above the change points of BMI with relative flat trend (P for non-linearity < 0.001). The change points of BMI estimated from piecewise linear models were of 28.16 kg/m 2 , 28.10 kg/m 2 , and 28.23 kg/m 2 in univariable and multivariable adjusted logistic regression models (Table 3 ). Table 3 Estimated change points in the association between BMI and RBD in PD below and above the change point from piecewise two-line models BMI change point (kg/m 2 ) OR per 1 kg/m 2 BMI increase below change point (95% CI) OR per 1 kg/m 2 BMI increase above change point (95% CI) Model 1 a 28.16 1.07 (1.01–1.14) 0.99 (0.95–0.9986) Model 2 b 28.10 1.07 (1.01–1.15) 1.01 (0.99–1.00) Model 3 c 28.23 1.08 (1.003–1.16) 1.00 (0.95–1.00) a: No covariate was adjusted. b: Age, sex, PD duration, GDS-15 score, LEDD, hypertension, and MDS-UPDRS I, II and III scores were adjusted. c: Education level, serum uric acid, Hoehn & Yahr stage, motor subtype, STAI, SCOPA-AUT, and MSEADL scores were additionally adjusted. Abbreviations: BMI, body mass index; PD, Parkinson’s disease; OR: odds ratio; CI: confidence interval; RBD, rapid eye movement sleep behavior disorder; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson’s Disease Rating Scale; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson’s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living. The effect modification by depression on the association between BMI and RBD in PD was found in this study. In individuals with depression, we found that the relationship between BMI and RBD was linear with the risk of RBD increasing continuously with higher BMI (OR: 1.08, 95% CI 1.002 to 1.18, P for non-linearity = 0.15; Fig. 3 A). In individuals without depression, the threshold effect analysis of BMI on RBD was further performed by the two-piecewise linear regression. The risk of RBD increased rapidly until BMI of 28.31 kg/m 2 (OR:1.10, 95% CI: 1.01 to 1.19) with minimal change afterwards (OR:0.98, 95% CI: 0.93 to 1.00, P for non-linearity = 0.001; Fig. 3 A). We found no evidence of effect modification by sex, motor subtype and hypertension on the association between BMI and RBD in PD (Fig. 3 B-D, e-Table 1 in supplementary material 2). The results of sensitivity analyses were similar to the main analysis, when excluding participants with MOCA score less than 26, restricting the analysis to individuals in the first and second categories of education, or excluding other ethnic groups, which indicating the robustness of our results (e-Figure 1 in supplementary material 2). 4. Discussion To our knowledge, this was the first study to investigate the association between BMI and non-motor symptom of PD. In the current study, we examined the association between RBD and BMI among PD patients with use of PPMI cross-sectional data. Our findings showed the significant presence of dose-response relationship between baseline BMI and RBD in PD patients. BMI tended to be positively associated with the risk of RBD up to change points with little association above these change points. And we estimated an increased risk of RBD among obese individuals compared with those of healthy weight in PD. Although the study confirmed the nonlinear association between BMI and RBD in PD, the mechanisms did not seem straightforward. It has been reported that the pathology of PD originates from the gastrointestinal tract, which also serves as an energy portal, and develops upward along the neural pathway to the central nervous system, including the dorsal motor nucleus of substantia nigra, vagus, and hypothalamus. These areas are also involved in energy metabolism control and sleep regulation 30,31 . Some studies have suggested that altered sleep quality and circadian clock could directly affect neuro-endocrine systems involved in the regulation of energy balance, resulting in overweight. This includes the sympathetic nervous system, HPA axis, brain metabolism, leptin, and ghrelin levels 32 . Furthermore, it has been found that a lack of orexin or insufficient orexin signaling, along with low physical activity, might promote the coexistence of sleep disorders and overweight 33 . Conversely, RBD itself might predispose individuals to worsening overweight due to sleep deprivation, increased sympathetic activation, sleep fragmentation, ineffective sleep, and insulin resistance. This could potentially lead to diabetes and aggravation of obesity 34,35 . Thus, it seems that RBD and weight status in the context of PD form a cycle interrelating with each other. It is crucial to understand the precise interaction between them. Subgroup analyses showed strong interactions of depression and the clear heterogeneity in the associations of BMI and RBD, meaning that the association of RBD with BMI would be affected by the mood status of PD patients. The risk of RBD was higher for obese individuals with depression compared to those without depression, which was partly consistent with results from some major studies. It has been shown that patients with depression exhibit abnormalities in sleep parameters throughout all phases of sleep architecture. Among these alterations, changes in REM sleep are particularly prominent and are often considered a distinct biological marker of depression 36 . Patients with depression may experience longer disease duration, more severe symptoms, and a higher overall illness severity compared to individuals without depression. As a result, they are at a higher risk of developing RBD 37 . Several studies investigating the pathophysiological mechanisms underlying the relationship between sleep disturbance and depression have found that dysregulation of monoamine neurotransmitters such as serotonin, norepinephrine, and dopamine, which are responsible for REM sleep abnormalities, is also associated with the presentation of depression 38,39 . Furthermore, sleep disorders can increase markers of inflammation by activating the sympathetic nervous system and β-adrenergic signaling, leading to increased NF-κB activity and activation of inflammatory gene expression. Notably, there is a strong association between inflammation and depression 40,41 . To better understand the link between sleep and depression, further research is needed to elucidate the role of inflammation, monoamines, and other related neurotransmitters. There were several limitations to this study. First, bias was inevitable in cross-sectional studies and prospective and large cohort studies are still needed in the future. Second, although we tried to adjust many variables that can be related to RBD in PD, factors such as smoking and drinking could not be include due to the restriction of data source in PPMI. Third, in our participants, the number of patients with BMI < 18·5kg/m² was relatively small, which should be considered when interpreting results. 5. Conclusion In conclusion, this study demonstrated a significant dose-response association between BMI and RBD in PD patients, even after adjusting for potential confounders. And there was a depression-based difference in the impact of BMI on RBD. Regular monitoring of BMI is thus important for patients with PD, particularly patients with depression. Future randomized controlled trials or cohort studies are needed to validate these findings and provide more precise prevention and treatment options for RBD in PD. Abbreviations BMI, body mass index; REM, rapid eye movement; RBD, rapid eye movement sleep behaviour disorder; RBDSQ, rapid eye movement sleep behaviour disorder screening questionnaire; PD, Parkinson’s disease; PPMI, Parkinson's Progression Markers Initiative; RCS, restricted cubic spline; SWEDD, scans without evidence of dopaminergic deficit; MDS, Movement Disorders Society; LEDD, levodopa equivalent daily dose; UPDRS, Unified Parkinson’s Disease Rating Scale; SCOPA-AUT, Scale for Outcomes for Parkinson’s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living Scale; CSF, cerebrospinal fluid; TD, tremor dominate; PIGD, postural instability/gait disorder; MOCA, Montreal Cognitive Assessment; GDS-15, 15-item Geriatric Depression Scale; SD, standard deviation; IQR, interquartile range; CI, confidence interval. Declarations Statements and Declarations Competing Interests The authors declare that they have no conflict of interest. Funding This work was supported by Shanghai Science and Technology Committee [grant number 21Y31920300]; National Natural Science Foundation of China [grant number 82074355]; Shanghai Pujiang Program [grant number 2020PJD066]; Shanghai Sailing Program [grant number 2021YF1447800], and Shanghai Municipal Health Commission [grant number 20204Y0168]. Funders had no role in study design, data collection, analysis, or decision to publish the manuscript. PPMI (a public–private partnership) is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Allergan, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, Voyager Therapeutics and Golub Capital. Funders had no role in design, analysis, or decision of publishment. Data availability The datasets generated during and/or analysed during the current study are available from PPMI database (www.ppmi-info.org/data) Acknowledgements Not applicable. Author contributions Conceptualization: Yang Cao, Qing Ye; Methodology: Si-Chun Gu, Xiao-Lei Yuan, Ping Yin; Formal analysis and investigation: Si-Chun Gu; Writing - original draft preparation: Si-Chun Gu, Xiao-Lei Yuan, Ping Yin; Writing - review and editing: Yuan-Yuan Li, Chang-De Wang, Min-Jue Gu, Li-Min Xu, Chen Gao, You Wu, Yu-Qing Hu, Can-Xing Yuan; Funding acquisition: Can-Xing Yuan, Qing Ye, Si-Chun Gu, Xiao-Lei Yuan; Supervision: Can-Xing Yuan, Qing Ye, Si-Chun Gu, Xiao-Lei Yuan, Ping Yin. All authors approved the final version of the manuscript. References Collaborators, G. B. D. O. et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med 377, 13–27 (2017). https://doi.org:10.1056/NEJMoa1614362 Berrington de Gonzalez, A. et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 363, 2211–2219 (2010). https://doi.org:10.1056/NEJMoa1000367 Parr, C. L. et al. 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P., Jr. Role of sleep and circadian disruption on energy expenditure and in metabolic predisposition to human obesity and metabolic disease. Obes Rev 18 Suppl 1, 15–24 (2017). https://doi.org:10.1111/obr.12503 Schmid, S. M. et al. Sleep loss alters basal metabolic hormone secretion and modulates the dynamic counterregulatory response to hypoglycemia. J Clin Endocrinol Metab 92, 3044–3051 (2007). https://doi.org:10.1210/jc.2006-2788 Rutters, F. et al. Distinct associations between energy balance and the sleep characteristics slow wave sleep and rapid eye movement sleep. Int J Obes (Lond) 36, 1346–1352 (2012). https://doi.org:10.1038/ijo.2011.250 Liu, M. et al. Potential Crosstalk Between Parkinson's Disease and Energy Metabolism. Aging Dis 12, 2003–2015 (2021). https://doi.org:10.14336/AD.2021.0422 Long, K. et al. Study on the Clinical Features of Parkinson's Disease With Probable Rapid Eye Movement Sleep Behavior Disorder. Front Neurol 11, 979 (2020). https://doi.org:10.3389/fneur.2020.00979 Fang, H., Tu, S., Sheng, J. & Shao, A. Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. J Cell Mol Med 23, 2324–2332 (2019). https://doi.org:10.1111/jcmm.14170 Wang, Y. Q. et al. The Neurobiological Mechanisms and Treatments of REM Sleep Disturbances in Depression. Curr Neuropharmacol 13, 543–553 (2015). https://doi.org:10.2174/1570159x13666150310002540 McCall, W. V. et al. Treatment of insomnia in depressed insomniacs: effects on health-related quality of life, objective and self-reported sleep, and depression. J Clin Sleep Med 6, 322–329 (2010). Adrien, J. Neurobiological bases for the relation between sleep and depression. Sleep Med Rev 6, 341–351 (2002). Irwin, M. R. & Cole, S. W. Reciprocal regulation of the neural and innate immune systems. Nat Rev Immunol 11, 625–632 (2011). https://doi.org:10.1038/nri3042 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1STROBE.docx Supplementarymaterial2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3761895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266435777,"identity":"0bd86bc2-e65e-45ed-9839-b11dd5e541f0","order_by":0,"name":"Si-Chun Gu","email":"","orcid":"","institution":"Longhua Hospital Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Si-Chun","middleName":"","lastName":"Gu","suffix":""},{"id":266435778,"identity":"62d93f07-72c5-4413-b016-3e628dedea84","order_by":1,"name":"Xiao-Lei Yuan","email":"","orcid":"","institution":"Longhua Hospital Shanghai University 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05:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3761895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3761895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49639708,"identity":"e6b873a3-7522-41ca-838d-025acb0c82d0","added_by":"auto","created_at":"2024-01-15 18:55:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243096,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population. PPMI: Parkinson's Progression Markers Initiative; PD: Parkinson’s disease; SWEDD: scans without evidence of dopaminergic deficit; RBDSQ: Rapid eye movement (REM) sleep behavior disorder questionnaire; BMI: body mass index.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/aadd00e22a40897cda1ddc6f.png"},{"id":49639710,"identity":"08ad113f-0d34-4081-b2b4-03eafb23740e","added_by":"auto","created_at":"2024-01-15 18:55:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":621263,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic spline for the association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease. Odds ratios were indicated by solid lines and 95% confidence interval by shaded areas based on the restricted cubic spline models. For A, rapid eye movement sleep behavior disorder was not adjusted for any covariate. For B and C, adjusted factors were consistent with model 2 and model 3. All figures were created with the use of R software (version 3.3.3, \u003ca href=\"https://www.r-project.org/\"\u003ehttps://www.r-project.org/\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/da0c635eb98dbccbe37d5867.png"},{"id":49639709,"identity":"a01f6f05-6c57-4460-b231-81be96ada638","added_by":"auto","created_at":"2024-01-15 18:55:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":330944,"visible":true,"origin":"","legend":"\u003cp\u003eEffect modification of depression (A), motor subtype (B), sex (C), and hypertension (D) on the association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease. Odds ratios were indicated by solid lines and 95% confidence interval by shaded areas based on the restricted cubic spline models. Estimates were adjusted for age, sex, Parkinson’s Diseaseduration, 15-item Geriatric Depression Scale score, levodopa equivalent daily dose, hypertension, depression, Unified Parkinson’s Disease Rating Scale I, II and III scores, education level, serum uric acid, Hoehn \u0026amp; Yahr stage, State-Trait Anxiety Inventory score, Scale for Outcomes for Parkinson’s Disease-autonomic function score, and Modified Schwab and England Activities of Daily Living Scale score. All figures were created with the use of R software (version 3.3.3, \u003ca href=\"https://www.r-project.org/\"\u003ehttps://www.r-project.org/\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/3b9576ebb7acb0b7d4dd8d46.png"},{"id":51436265,"identity":"a082f370-8fec-4b91-97f3-fd259ec43fe4","added_by":"auto","created_at":"2024-02-21 15:18:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":823203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/0f8f1c05-38c3-4a3d-81bc-304cce4aadce.pdf"},{"id":49639711,"identity":"b52eaabc-efa2-4496-be0c-eba51bdaf668","added_by":"auto","created_at":"2024-01-15 18:55:32","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":35810,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1STROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/302859aa89e5ec01022596f7.docx"},{"id":49639712,"identity":"ba20c9a5-d27a-4718-99b3-95cf328bf11d","added_by":"auto","created_at":"2024-01-15 18:55:32","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1110368,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3761895/v1/a23681eb816e67b6b9fed4e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBody mass index (BMI) is a widely used measure of relative body weight and is considered the gold standard of general nutritional status\u003csup\u003e1\u003c/sup\u003e. Both high and low BMI are directly related to health risks including hypertension, type 2 diabetes, cardiovascular disease, and certain cancers, and can result in detrimental health outcomes. A change in BMI is considered an important outcome in monitoring health and well-being\u003csup\u003e2,3\u003c/sup\u003e. BMI has been found to be associated with function in several neurodegenerative diseases. In patients with amyotrophic lateral sclerosis, a BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e is associated with reduced survival, while a BMI of 30\u0026ndash;35 kg/m\u003csup\u003e2\u003c/sup\u003e is associated with increased survival\u003csup\u003e4\u003c/sup\u003e. In patients with dementia, a low BMI is associated with reduced survival and serves as an independent predictor of mortality, regardless of cognitive impairment severity\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a neurological degenerative disease characterized by motor symptoms including rigidity, tremor, bradykinesia, and postural instability, along with a wide range of non-motor symptoms, including sleep disturbances, autonomic dysfunction, neuropsychiatric disorders, and cognitive impairment\u003csup\u003e6\u003c/sup\u003e. The combination of the heterogeneous symptomatology mentioned above directly threatens the ability of individuals with PD to live independently and imposes a significant economic and global burden\u003csup\u003e7,8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the high prevalence of weight variation in PD, increasing attention is being paid to investigate the role of BMI in PD. For the diagnosis of PD, several studies have associated obesity with a higher risk of developing PD. Conversely, patients with PD are consistently reported to have lower body weight compared to healthy controls\u003csup\u003e9\u003c/sup\u003e. When considering survival in PD, there is a significant inverse dose-response relationship between baseline BMI and mortality. BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 kg/m\u003csup\u003e2\u003c/sup\u003e contributes to extended survival rates, while BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e is linked with poor survival\u003csup\u003e10\u003c/sup\u003e. The association between BMI and motor symptoms of PD has been explored, that is decreasing-BMI is associated with worse scores over time in UPDRS motor scores, whereas increasing-BMI is associated with better UPDRS motor scores\u003csup\u003e11\u003c/sup\u003e. Although the biological mechanisms have not been identified, potential contributors may include perturbation of hypothalamic metabolic regulation, gastrointestinal dysfunction, and alteration of energy expenditure and food intake\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, to the best of our knowledge, the effects of BMI on PD non-motor symptoms have not been previously studied. As the most common disabling non-motor symptom of PD, the prevalence of rapid eye-movement (REM) sleep-related behavioral disorder (RBD) is around 20\u0026ndash;50% in PD patients\u003csup\u003e13\u003c/sup\u003e. RBD is characterized by a loss of muscle atonia during REM sleep, leading to dream enactment behaviors that are frequently injurious to patients and their partners\u003csup\u003e14,15\u003c/sup\u003e. Previous studies have clearly observed U-shaped and inverse U-shaped relationships between sleep duration and BMI\u003csup\u003e16\u003c/sup\u003e. However, the impact of BMI on RBD in PD has not been fully clarified, and understanding is crucial for improving outcomes related to non-motor symptoms in patients with PD. Therefore, the objective of this study was to investigate the association between baseline BMI and RBD in patients with PD using a large population-based dataset.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eStudy data used in the present study were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ppmi-info.org/data\" target=\"_blank\"\u003ewww.ppmi-info.org/data\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ppmi-info.org/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PPMI is an ongoing observational, international, multicentre cohort study aiming to identify blood-based, genetic, spinal fluid, and imaging biomarkers of Parkinson\u0026rsquo;s disease (PD) progression with longitudinal follow-up in a large cohort. The aims and methodology of PPMI study have been published elsewhere\u003csup\u003e17\u003c/sup\u003e. Study protocol and manuals and are available online. The study was approved by the Institutional Review Board at each site, and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003e For this study, we utilized the baseline dataset of PPMI from 33 participating outpatient PD treatment centers worldwide on the basis of inclusion and exclusion criteria previously published. All the methods were performed in accordance with relevant institutional guidelines and regulations. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guideline was included in supplementary material 1. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrated the selection process of our study. Participants who were diagnosed as idiopathic PD, with baseline Rapid eye movement (REM) sleep behavior disorder questionnaire (RBDSQ) score and BMI data were included as study population. Healthy controls, scans without evidence of dopaminergic deficit (SWEDD) patients, prodromal patients, duplicated participants, individuals with missing baseline RBDSQ score or BMI data were excluded from this analysis. In total, 1115 patients with complete information, enrolled between November 2010 and June 2023, were included in our analyses. The data were downloaded on September 15, 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Exposure and outcome\u003c/h2\u003e \u003cp\u003eExposure was assigned as baseline BMI. Anthropometric data, including height and weight, were obtained from baseline data. BMI was calculated as weight divided by height squared (weight(kg)/height(m\u003csup\u003e2\u003c/sup\u003e)), and then classified into 4 WHO categories, including underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) categories\u003csup\u003e18,19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOutcome was assigned as REM-sleep behavior disorder (RBD) at baseline. The 10-item RBDSQ has been validated in PD patients and demonstrates good accuracy in identifying RBD. Items 1 to 4 of the RBDSQ assess the frequency and content of dreams, as well as their relationship to nocturnal movements and behavior. Item 5 asks about self-injuries and injuries to the bed partner. Item 6 consists of four subitems specifically assessing nocturnal motor behavior, including questions about nocturnal vocalization, sudden limb movements, complex movements, or items falling from the bed. Items 7 and 8 inquire about nocturnal awakenings. Item 9 focuses on general sleep disturbances, while item 10 pertains to the presence of any neurological disorder. The maximum total score on the RBDSQ is 13 points. Following the definition set by the International Parkinson and Movement Disorders Society (MDS) Task Force, we defined RBD as a baseline RBDSQ score equal to or greater than 5\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Covariates\u003c/h2\u003e \u003cp\u003eTo assess the potential influence of confounding factors, several important covariates were selected as a prior based on the literature. These covariates included age, sex, PD duration, depression (measured by the 15-item Geriatric Depression Scale [GDS-15] score in PPMI), levodopa equivalent daily dose (LEDD), hypertension (defined as self-reported hypertension, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or use of antihypertensive drugs), and MDS Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS) score at baseline\u003csup\u003e21\u003c/sup\u003e. All participants in the PPMI study underwent the standard test battery of assessments. In addition to the covariates mentioned above, sociodemographic characteristics and clinical battery relevant to this study including education, Hoehn \u0026amp; Yahr stage, serum uric acid, autonomic function assessed by the Scale for Outcomes for Parkinson\u0026rsquo;s Disease-autonomic function (SCOPA-AUT) score, anxiety assessed by State-Trait Anxiety Inventory (STAI) score, and daily living quality assessed by Modified Schwab and England Activities of Daily Living Scale (MSEADL) were adjusted as potential confounders in the models. Although PPMI collected an array of cerebrospinal fluid (CSF) biomarkers, these measures were only available for a small subset of participants and thus were not included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eSummary statistics were performed and tested for normality (Shapiro-Wilk). Continuous data were presented as median (interquartile range [IQR]) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), with categorical data presented as proportion and number (N [%]) as appropriate. Group comparisons were analyzed with use of Student\u0026rsquo;s t tests or Wilcoxon\u0026rsquo;s rank-sum tests for continuous data and Chi-square tests or Fisher\u0026rsquo;s exact tests for categorical data. Data were more than 99% complete. The remaining missing values were imputed by multivariable chained imputation with fully conditional specification, and imputed and reported results were similar\u003csup\u003e22\u003c/sup\u003e. All statistical tests were two-sided and the level of significance was set at 0.05.\u003c/p\u003e \u003cp\u003eParticipants were divided into two groups based on whether they had RBD. The relationship between BMI and RBD was examined using logistic regression models. The adjustment was accomplished via 3 models: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) model 1, without any covariate adjustment; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) model 2, adjusted for age, sex, PD duration, GDS-15 score, LEDD, hypertension, depression, and MDS-UPDRS I, II and III scores; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) model 3, additionally adjusted for education level, serum uric acid, Hoehn \u0026amp; Yahr stage, STAI, SCOPA-AUT, and MSEADL scores as covariates. The results were presented as odds ratios (OR) with corresponding 95% confidence intervals (CI).\u003c/p\u003e \u003cp\u003eRestricted cubic spline (RCS) analysis was also performed to examine the association of between baseline BMI and RBD based on univariable and multivariable adjusted logistic regression models\u003csup\u003e23\u003c/sup\u003e. To balance best fit and overfitting in the main splines, the number of knots, between three and seven, was chosen as the lowest value for the Akaike information criterion. If the difference in the number of knots was within two for different models, the lowest number of knots was selected\u003csup\u003e24\u003c/sup\u003e. The same number of knots from the main splines was also applied for stratified analyses to allow direct comparison of overall and stratified analyses, including test of interaction. We tested for potential non-linearity by using a likelihood ratio test comparing the model with only a linear term against the model with linear and cubic spline terms. Piecewise-linear models were then fitted to quantify the association between BMI and RBD. If evidence of non-linearity was found, a two-line piecewise linear model with a single change point was estimated by trying all possible values for the change point and selecting the value with the highest likelihood among those considered, while accounting for covariates.\u003c/p\u003e \u003cp\u003eWe fitted interactions to investigate effect modification by depression (with depression, without depression, GDS-15\u0026thinsp;\u0026ge;\u0026thinsp;5 or not), motor subtype (tremor dominate [TD], non-TD including postural instability/gait disorder [PIGD] or Indeterminate), sex (male, female), and hypertension (yes, no) \u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. Due to the nonlinear association between BMI and RBD in the whole participants, we used continuous BMI and the quadratic term BMI\u003csup\u003e2\u003c/sup\u003e in multivariable adjusted logistic regression models (model 3) to allow for the nonlinearity during the interaction analyses. The first model to test for the depression-by-BMI interaction allowed for interaction with both the linear and quadratic terms of BMI\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModel A: RBD\u0026thinsp;=\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;BMI\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;depression\u0026thinsp;+\u0026thinsp;BMI \u0026times; depression\u0026thinsp;+\u0026thinsp;BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; depression\u0026thinsp;+\u0026thinsp;other covariates\u003c/p\u003e \u003cp\u003eIn the absence of interaction with the quadratic term, the model was then simplified to only allow for interaction with the linear term.\u003c/p\u003e \u003cp\u003eModel B: CVD\u0026thinsp;=\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;BMI\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;subtype\u0026thinsp;+\u0026thinsp;BMI \u0026times; depression\u0026thinsp;+\u0026thinsp;other covariates\u003c/p\u003e \u003cp\u003eThe significance of the interaction was determined based on the highest level interaction term in the models, and here, lack of interaction was inferred when neither BMI\u003csup\u003e2\u003c/sup\u003e \u0026times; depression (Model A) nor BMI \u0026times; depression (Model B) were significant at the 5% level. Interactions by subtype, hypertension, and sex were examined in the corresponding manner, replacing \u0026ldquo;depression\u0026rdquo; above with hypertension, sex or subtype as relevant.\u003c/p\u003e \u003cp\u003eTo assess the robustness of the results, we additionally applied three sensitivity analyses. First, we examined the shape of BMI-RBD relation after excluding individuals who had a baseline Montreal Cognitive Assessment (MOCA) score less than 26 as the definition of cognitive impairment\u003csup\u003e29\u003c/sup\u003e. Second, we restricted the analysis to individuals in the first and second categories of education. Third, we performed the analysis after excluding other ethnic groups. All data were analyzed using R (version 4.0.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOf 1115 participants included in the study, we identified 426 (38.2%) RBD at baseline. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the baseline characteristics of participants according to BMI categories. The proportion of underweight, normal, overweight and obese at baseline was 2.61%, 36.59%, 40.36% and 20.44%, respectively. The median BMI was 26.1, and the median age was 63.6 years. Within 4 BMI categories, significant differences were observed regarding the proportion of RBD, RBDSQ score, sex, education status, MOCA score, GDS-15 score, SCOPA-AUT score, proportion of hypertension, and serum uric acid. The individuals with higher BMI tended to have higher RBDSQ score, as well as a higher proportion of RBD. The proportion of RBD decreased with higher BMI. The proportion of male decreased with higher BMI. The individuals with lower BMI tended to have a lower proportion of the first category of education (\u0026lt;\u0026thinsp;13 years) and a higher proportion of the second category of education (13\u0026ndash;23 years). Underweight individuals had higher MOCA score, GDS-15 score, and SCOPA-AUT score than others. Serum uric acid increased with higher BMI, with the median of 4.1 mg/dL in underweight individuals, 4.5 mg/dL in normal weight individuals, 5.4 mg/dL in overweight individuals, and 5.5 mg/dL in obese individuals. Moreover, a positive association was observed between hypertension and BMI. 17.2% of underweight individuals had hypertension, whereas 49.6% of obese individuals had hypertension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study population according to BMI measure by WHO BMI category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1115\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;18\u0026middot;5kg/m\u0026sup2;)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003cp\u003e(18\u0026middot;5\u0026ndash;24\u0026middot;9kg/\u0026sup2;)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;408\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003cp\u003e(25\u0026middot;0\u0026ndash;29\u0026middot;9kg/m\u0026sup2;)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;450\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;30\u0026middot;0kg/m\u0026sup2;)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;228\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1 [23.9\u0026ndash;29.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9 [16.9\u0026ndash;18.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3 [21.7\u0026ndash;24.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.3 [26.0\u0026ndash;28.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.2 [30.9\u0026ndash;34.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.6 [56.2\u0026ndash;69.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0 [51.5\u0026ndash;69.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.2 [54.7\u0026ndash;69.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.2 [57.1\u0026ndash;70.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.2 [56.8\u0026ndash;69.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e435 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e680 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320 (71.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143 (62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0 [14.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0 [15.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0 [14.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0 [14.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0 [13.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;23 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e904 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (89.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e354 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e177 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;23 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1043 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385 (94.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e415 (92.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (94.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8 [3.7\u0026ndash;20.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7 [3.5\u0026ndash;14.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6 [4.2\u0026ndash;22.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1 [3.5\u0026ndash;19.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2 [4.2\u0026ndash;22.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.8\u0026thinsp;\u0026plusmn;\u0026thinsp;317.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.1\u0026thinsp;\u0026plusmn;\u0026thinsp;366.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.1\u0026thinsp;\u0026plusmn;\u0026thinsp;335.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.4\u0026thinsp;\u0026plusmn;\u0026thinsp;292.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147.0\u0026thinsp;\u0026plusmn;\u0026thinsp;328.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoehn \u0026amp; Yahr Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e713 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261 (64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290 (64.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e742 (66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e294 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-TD (PIGD or Indeterminate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS-UPDRS I score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 [3.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 [3.0\u0026ndash;11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 [3.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0 [3.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0 [3.0\u0026ndash;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS-UPDRS II score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 [3.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 [3.0\u0026ndash;11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 [3.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0 [2.0\u0026ndash;9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5 [3.0\u0026ndash;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS-UPDRS III score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0 [14.0\u0026ndash;27.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 [15.0\u0026ndash;29.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5 [14.0\u0026ndash;26.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0 [14.0\u0026ndash;27.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.0 [15.0\u0026ndash;29.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal MDS-UPDRS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0 [23.0\u0026ndash;43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0 [25.0\u0026ndash;50.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5 [23.0\u0026ndash;43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.0 [23.0\u0026ndash;42.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.0 [24.0\u0026ndash;47.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBDSQ score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 [2.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 [2.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 [2.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 [2.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0 [3.0\u0026ndash;7.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith RBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOCA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSEADL score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 [1.0\u0026ndash;3.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 [2.0\u0026ndash;5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 [0.0\u0026ndash;3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0 [0.0\u0026ndash;3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0 [1.0\u0026ndash;4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0 [50.0\u0026ndash;77.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.0 [58.0\u0026ndash;91.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.0 [51.0\u0026ndash;77.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.5 [49.0\u0026ndash;76.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.0 [49.0\u0026ndash;78.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCOPA-AUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 [6.0\u0026ndash;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 [6.0\u0026ndash;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 [6.0\u0026ndash;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0 [5.0\u0026ndash;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.0 [6.0\u0026ndash;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Uric Acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 [4.2\u0026ndash;5.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 [3.4\u0026ndash;5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 [3.9\u0026ndash;5.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4 [4.5\u0026ndash;6.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5 [4.5\u0026ndash;6.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData were presented as N (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median [IQR]. Abbreviations: BMI, body mass index; PD, Parkinson\u0026rsquo;s disease; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson\u0026rsquo;s Disease Rating Scale; RBDSQ, rapid eye movement sleep behavior disorder screening questionnaire; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson\u0026rsquo;s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living; MOCA, Montreal Cognitive Assessment; N, number; IQR, interquartile range; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the association of BMI and RBD. In the crude logistic regression model, obese individuals had an OR of 1.73 (95% CI 1.24 to 2.41) for RBD compared with normal weight individuals, which indicated that the risk of RBD was increasing for rise in BMI. The strong positive association between BMI and RBD consistently existed in adjusted models. Compared with those of normal weight, obese individuals had ORs of 1.63 (95% CI 1.14 to 2.34) and 1.77 (95% CI 1.21 to 2.59) for RBD in model 2 and model 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between baseline BMI and the incidence of RBD in PD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvents/Participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRBD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107/228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.24\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169/450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.89\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38 (0.63\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138/408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107/228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 (1.14\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169/450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.84\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (0.52\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138/408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107/228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.21\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169/450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.85\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.48\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138/408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea: No covariate was adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eb: Age, sex, PD duration, GDS-15 score, LEDD, hypertension, and MDS-UPDRS I, II and III scores were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ec: Education level, serum uric acid, Hoehn \u0026amp; Yahr stage, motor subtype, STAI, SCOPA-AUT, and MSEADL scores were additionally adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: BMI, body mass index; PD, Parkinson\u0026rsquo;s disease; OR: odds ratio; CI: confidence interval; RBD, rapid eye movement sleep behavior disorder; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson\u0026rsquo;s Disease Rating Scale; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson\u0026rsquo;s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe used RCS to flexibly model and visualize the relation of BMI with RBD in PD with three knots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). For the risk of RBD, there was evidence of non-linearity with two main patterns seen: we found positive associations below the change points of BMI with the rapid increase of the risk of RBD, whereas there was little evidence of association above the change points of BMI with relative flat trend (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The change points of BMI estimated from piecewise linear models were of 28.16 kg/m\u003csup\u003e2\u003c/sup\u003e, 28.10 kg/m\u003csup\u003e2\u003c/sup\u003e, and 28.23 kg/m\u003csup\u003e2\u003c/sup\u003e in univariable and multivariable adjusted logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated change points in the association between BMI and RBD in PD below and above the change point from piecewise two-line models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI change point (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR per 1 kg/m\u003csup\u003e2\u003c/sup\u003e BMI increase below change point (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR per 1 kg/m\u003csup\u003e2\u003c/sup\u003e BMI increase above change point (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.01\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.95\u0026ndash;0.9986)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.01\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08 (1.003\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.95\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ea: No covariate was adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eb: Age, sex, PD duration, GDS-15 score, LEDD, hypertension, and MDS-UPDRS I, II and III scores were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ec: Education level, serum uric acid, Hoehn \u0026amp; Yahr stage, motor subtype, STAI, SCOPA-AUT, and MSEADL scores were additionally adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: BMI, body mass index; PD, Parkinson\u0026rsquo;s disease; OR: odds ratio; CI: confidence interval; RBD, rapid eye movement sleep behavior disorder; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorders Society Unified Parkinson\u0026rsquo;s Disease Rating Scale; GDS-15, 15-item Geriatric Depression Scale; SCOPA-AUT, Scale for Outcomes for Parkinson\u0026rsquo;s Disease-autonomic function; STAI, State-Trait Anxiety Inventory; MSEADL, Modified Schwab and England Activities of Daily Living.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe effect modification by depression on the association between BMI and RBD in PD was found in this study. In individuals with depression, we found that the relationship between BMI and RBD was linear with the risk of RBD increasing continuously with higher BMI (OR: 1.08, 95% CI 1.002 to 1.18, P for non-linearity\u0026thinsp;=\u0026thinsp;0.15; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In individuals without depression, the threshold effect analysis of BMI on RBD was further performed by the two-piecewise linear regression. The risk of RBD increased rapidly until BMI of 28.31 kg/m\u003csup\u003e2\u003c/sup\u003e (OR:1.10, 95% CI: 1.01 to 1.19) with minimal change afterwards (OR:0.98, 95% CI: 0.93 to 1.00, P for non-linearity\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We found no evidence of effect modification by sex, motor subtype and hypertension on the association between BMI and RBD in PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D, e-Table\u0026nbsp;1 in supplementary material 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of sensitivity analyses were similar to the main analysis, when excluding participants with MOCA score less than 26, restricting the analysis to individuals in the first and second categories of education, or excluding other ethnic groups, which indicating the robustness of our results (e-Figure 1 in supplementary material 2).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this was the first study to investigate the association between BMI and non-motor symptom of PD. In the current study, we examined the association between RBD and BMI among PD patients with use of PPMI cross-sectional data.\u003c/p\u003e \u003cp\u003eOur findings showed the significant presence of dose-response relationship between baseline BMI and RBD in PD patients. BMI tended to be positively associated with the risk of RBD up to change points with little association above these change points. And we estimated an increased risk of RBD among obese individuals compared with those of healthy weight in PD.\u003c/p\u003e \u003cp\u003eAlthough the study confirmed the nonlinear association between BMI and RBD in PD, the mechanisms did not seem straightforward. It has been reported that the pathology of PD originates from the gastrointestinal tract, which also serves as an energy portal, and develops upward along the neural pathway to the central nervous system, including the dorsal motor nucleus of substantia nigra, vagus, and hypothalamus. These areas are also involved in energy metabolism control and sleep regulation \u003csup\u003e30,31\u003c/sup\u003e. Some studies have suggested that altered sleep quality and circadian clock could directly affect neuro-endocrine systems involved in the regulation of energy balance, resulting in overweight. This includes the sympathetic nervous system, HPA axis, brain metabolism, leptin, and ghrelin levels \u003csup\u003e32\u003c/sup\u003e. Furthermore, it has been found that a lack of orexin or insufficient orexin signaling, along with low physical activity, might promote the coexistence of sleep disorders and overweight \u003csup\u003e33\u003c/sup\u003e. Conversely, RBD itself might predispose individuals to worsening overweight due to sleep deprivation, increased sympathetic activation, sleep fragmentation, ineffective sleep, and insulin resistance. This could potentially lead to diabetes and aggravation of obesity \u003csup\u003e34,35\u003c/sup\u003e. Thus, it seems that RBD and weight status in the context of PD form a cycle interrelating with each other. It is crucial to understand the precise interaction between them.\u003c/p\u003e \u003cp\u003eSubgroup analyses showed strong interactions of depression and the clear heterogeneity in the associations of BMI and RBD, meaning that the association of RBD with BMI would be affected by the mood status of PD patients. The risk of RBD was higher for obese individuals with depression compared to those without depression, which was partly consistent with results from some major studies.\u003c/p\u003e \u003cp\u003eIt has been shown that patients with depression exhibit abnormalities in sleep parameters throughout all phases of sleep architecture. Among these alterations, changes in REM sleep are particularly prominent and are often considered a distinct biological marker of depression \u003csup\u003e36\u003c/sup\u003e. Patients with depression may experience longer disease duration, more severe symptoms, and a higher overall illness severity compared to individuals without depression. As a result, they are at a higher risk of developing RBD \u003csup\u003e37\u003c/sup\u003e. Several studies investigating the pathophysiological mechanisms underlying the relationship between sleep disturbance and depression have found that dysregulation of monoamine neurotransmitters such as serotonin, norepinephrine, and dopamine, which are responsible for REM sleep abnormalities, is also associated with the presentation of depression \u003csup\u003e38,39\u003c/sup\u003e. Furthermore, sleep disorders can increase markers of inflammation by activating the sympathetic nervous system and β-adrenergic signaling, leading to increased NF-κB activity and activation of inflammatory gene expression. Notably, there is a strong association between inflammation and depression \u003csup\u003e40,41\u003c/sup\u003e. To better understand the link between sleep and depression, further research is needed to elucidate the role of inflammation, monoamines, and other related neurotransmitters.\u003c/p\u003e \u003cp\u003eThere were several limitations to this study. First, bias was inevitable in cross-sectional studies and prospective and large cohort studies are still needed in the future. Second, although we tried to adjust many variables that can be related to RBD in PD, factors such as smoking and drinking could not be include due to the restriction of data source in PPMI. Third, in our participants, the number of patients with BMI\u0026thinsp;\u0026lt;\u0026thinsp;18\u0026middot;5kg/m\u0026sup2; was relatively small, which should be considered when interpreting results.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrated a significant dose-response association between BMI and RBD in PD patients, even after adjusting for potential confounders. And there was a depression-based difference in the impact of BMI on RBD. Regular monitoring of BMI is thus important for patients with PD, particularly patients with depression. Future randomized controlled trials or cohort studies are needed to validate these findings and provide more precise prevention and treatment options for RBD in PD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI,\u0026nbsp;body mass index;\u003c/p\u003e\n\u003cp\u003eREM, rapid eye movement;\u003c/p\u003e\n\u003cp\u003eRBD, rapid eye movement sleep behaviour disorder;\u003c/p\u003e\n\u003cp\u003eRBDSQ, rapid eye movement sleep behaviour disorder screening questionnaire;\u003c/p\u003e\n\u003cp\u003ePD, Parkinson\u0026rsquo;s disease;\u003c/p\u003e\n\u003cp\u003ePPMI,\u0026nbsp;Parkinson\u0026apos;s\u0026nbsp;Progression Markers Initiative;\u003c/p\u003e\n\u003cp\u003eRCS,\u0026nbsp;restricted cubic spline;\u003c/p\u003e\n\u003cp\u003eSWEDD,\u0026nbsp;scans without evidence of dopaminergic deficit;\u003c/p\u003e\n\u003cp\u003eMDS,\u0026nbsp;Movement Disorders Society;\u003c/p\u003e\n\u003cp\u003eLEDD,\u0026nbsp;levodopa equivalent daily dose;\u003c/p\u003e\n\u003cp\u003eUPDRS,\u0026nbsp;Unified Parkinson\u0026rsquo;s Disease Rating Scale;\u003c/p\u003e\n\u003cp\u003eSCOPA-AUT,\u0026nbsp;Scale for Outcomes for Parkinson\u0026rsquo;s Disease-autonomic function;\u003c/p\u003e\n\u003cp\u003eSTAI,\u0026nbsp;State-Trait Anxiety Inventory;\u003c/p\u003e\n\u003cp\u003eMSEADL,\u0026nbsp;Modified Schwab and England Activities of Daily Living Scale;\u003c/p\u003e\n\u003cp\u003eCSF,\u0026nbsp;cerebrospinal fluid;\u003c/p\u003e\n\u003cp\u003eTD,\u0026nbsp;tremor dominate;\u003c/p\u003e\n\u003cp\u003ePIGD,\u0026nbsp;postural instability/gait disorder;\u003c/p\u003e\n\u003cp\u003eMOCA,\u0026nbsp;Montreal Cognitive Assessment;\u003c/p\u003e\n\u003cp\u003eGDS-15, 15-item Geriatric Depression Scale;\u003c/p\u003e\n\u003cp\u003eSD, standard deviation;\u003c/p\u003e\n\u003cp\u003eIQR, interquartile range;\u003c/p\u003e\n\u003cp\u003eCI, confidence interval.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shanghai Science and Technology Committee [grant number 21Y31920300]; National Natural Science Foundation of China [grant number 82074355]; Shanghai Pujiang Program [grant number 2020PJD066]; Shanghai Sailing Program [grant number 2021YF1447800], and Shanghai Municipal Health Commission [grant number 20204Y0168]. Funders had no role in study design, data collection, analysis, or decision to publish the manuscript.\u003c/p\u003e\n\u003cp\u003ePPMI (a public\u0026ndash;private partnership) is funded by the Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research and funding partners, including Abbvie, Allergan, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Denali, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, Voyager Therapeutics and Golub Capital. Funders had no role in design, analysis, or decision of publishment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from\u0026nbsp;PPMI\u0026nbsp;database (www.ppmi-info.org/data)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Yang Cao, Qing Ye; Methodology: Si-Chun Gu, Xiao-Lei Yuan, Ping Yin; Formal analysis and investigation: Si-Chun Gu; Writing - original draft preparation: Si-Chun Gu, Xiao-Lei Yuan, Ping Yin; Writing - review and editing: Yuan-Yuan Li, Chang-De Wang, Min-Jue Gu, Li-Min Xu, Chen Gao, You Wu, Yu-Qing Hu, Can-Xing Yuan; Funding acquisition: Can-Xing Yuan, Qing Ye, Si-Chun Gu, Xiao-Lei Yuan; Supervision: Can-Xing Yuan, Qing Ye, Si-Chun Gu, Xiao-Lei Yuan, Ping Yin. All authors approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. O. \u003cem\u003eet al.\u003c/em\u003e Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med 377, 13\u0026ndash;27 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1056/NEJMoa1614362\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1056/NEJMoa1614362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerrington de Gonzalez, A. \u003cem\u003eet al.\u003c/em\u003e Body-mass index and mortality among 1.46 million white adults. 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Nat Rev Immunol 11, 625\u0026ndash;632 (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/nri3042\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/nri3042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"body mass index, rapid eye movement sleep behavior disorder, Parkinson’s Disease, Parkinson's Progression Markers Initiative, restricted cubic spline","lastPublishedDoi":"10.21203/rs.3.rs-3761895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3761895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson\u0026rsquo;s disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, a total of 1115 PD participants were enrolled from Parkinson's Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed at baseline. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 426 (38.2%) RBD at baseline. The proportion of underweight, normal, overweight and obese at baseline was 2.61%, 36.59%, 40.36% and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m\u003csup\u003e2\u003c/sup\u003e, 28.10 kg/m\u003csup\u003e2\u003c/sup\u003e, and 28.23 kg/m\u003csup\u003e2\u003c/sup\u003e derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe current study found a significant dose-response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients.\u003c/p\u003e","manuscriptTitle":"Association of Body Mass Index with rapid eye movement sleep behavior disorder in Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-15 18:55:27","doi":"10.21203/rs.3.rs-3761895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b00d7283-d259-4d24-8235-b1c264134c46","owner":[],"postedDate":"January 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28078588,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"},{"id":28078589,"name":"Biological sciences/Neuroscience/Neural ageing"},{"id":28078590,"name":"Health sciences/Diseases/Neurological disorders/Movement disorders/Parkinsons disease"}],"tags":[{"value":"featured","date":"2024-01-15 18:58:31"}],"updatedAt":"2024-02-21T15:18:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-15 18:55:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3761895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3761895","identity":"rs-3761895","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}