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Background: Previous studies have suggested a connection between impaired olfactory function and an increased risk of rapid eye movement sleep behavior disorder (RBD) in individuals diagnosed with Parkinson’s disease (PD). However, there is a gap in knowledge regarding the potential impact of olfactory dysfunction on the long-term patterns of sleep disorders among early PD patients. Methods: Data from the Parkinson’s Progression Markers Initiative program included 589 participants with assessments of sleep disorders using the Epworth Sleepiness Scale (ESS) and RBD Screening Questionnaire (RBDSQ). Olfactory dysfunction at baseline was measured using the University of Pennsylvania Smell Identification Test. Trajectories of sleep disorders over a 5-year follow-up were identified using group-based trajectory modeling, and the relationship between olfactory dysfunction and sleep disorder trajectories was examined through binomial logistic regression. Results: Two distinct trajectories of sleep disorders over the 5-year follow-up period were identified, characterized by maintaining a low or high ESS score and a low or high RBDSQ score. An inversion association was observed between olfactory function measures and trajectories of excessive daytime sleepiness (odds ratio [OR] = 0.97, 95% confidence interval [CI] 0.95, 1.00, p = 0.038), after controlling for potential covariates. Similarly, olfactory function showed a significant association with lower trajectories of probable RBD (OR = 0.96, 95% CI 0.94, 0.98, p = 0.001) among early PD individuals. Consistent findings were replicated across alternative analytical models. Conclusions: Our findings indicated that olfactory dysfunction was associated with unfavorable long-term trajectories of sleep disorders among early PD.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized by progressive and preferential loss of dopaminergic neurons in the substantia nigra [1]. In 2016, the global prevalence of PD was estimated at approximately 6.1 million individuals, highlighting the significant impact of this condition on public health [2]. The diagnosis of PD is characterized by bradykinesia in addition to either rest tremor, rigidity, or both [3]. In addition, PD is increasingly recognized for its non-motor symptoms, including disturbances in sleep patterns [4]. Sleep disorders, such as rapid eye movement (REM) sleep behavior disorder (RBD), excessive daytime sleepiness (EDS), and insomnia, are common among individuals with PD. These sleep disturbances can have a substantial impact on the overall quality of life for PD patients, affecting both their physical and mental well-being [5]. EDS is characterized by the inability to sustain wakefulness and alertness during the main waking episodes of the day, resulting in moments of irrepressible sleepiness or unintentional lapses into drowsiness or sleep [6], with a prevalence of 21–76% in PD patients [7]. RBD is a parasomnia featured by the intermittent loss of REM sleep electromyographic atonia and dream-enacting behavior [8]. Probable RBD (pRBD) is one of the non-motor symptoms with a prevalence of 16–47% in PD patients, which may precede the onset of motor symptoms [9, 10].

Olfactory dysfunction, or impaired sense of smell, has emerged as a noteworthy clinical marker in the early stages of PD [11], observed in 90% of early stage PD patients [12]. Olfactory dysfunction precedes the onset of motor features by years and potentially serves as a predictor for the conversion from prodromal PD to PD [13]. Given the established association between olfactory dysfunction and PD, there is growing interest in exploring the potential links between olfactory impairment and other non-motor symptoms, particularly sleep disorders, in PD [14].

Despite the recognition of the significance of sleep disorders in PD, the understanding of their long-term trajectories and potential associations with other non-motor symptoms, such as olfactory dysfunction, remains limited. Existing studies have primarily focused on assessing sleep disorders at single time points, rather than examining their patterns of change over time. Furthermore, there is a lack of longitudinal investigations exploring the role of olfactory dysfunction in the progression of sleep disorders among individuals with PD [15].

To this end, this study aimed to address these knowledge gaps by utilizing a 5-year longitudinal cohort from the Parkinson’s Progression Markers Initiative (PPMI) program to examine the relationship between olfactory dysfunction and the trajectories of sleep disorders, specifically EDS and RBD, in early PD patients. The study hypothesizes that olfactory dysfunction may be associated with unfavorable trajectories of these sleep disorders, shedding light on the potential interplay between olfactory impairment and non-motor symptoms in PD.

Study Design and Participants

The PPMI is an ongoing international multicenter cohort study that aims to investigate disease progression in PD [16, 17]. The study aimed to recruit individuals with early stage untreated (de novo) PD as well as healthy controls. Follow-up visits were conducted at regular intervals, with assessments every 3 months during the first year and every 6 months in subsequent years. For the purposes of this analysis, data from baseline and follow-up visits of 938 PD patients over 5 years were obtained as of June 12, 2023. The Institutional Review Board approved the research protocol at each participating site, and all participants provided informed written consent before enrollment. Additional information about the objectives, design, and methods of the PPMI study can be accessed on ppmiinfo.org. This current study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline [18].

Inclusion criteria for PD participants encompassed a clinical diagnosis of PD based on the presence of bradykinesia combined with either rest tremor, rigidity, or both [3, 19], Hoehn and Yahr (H and Y) stage ≤2 [20]. Individuals with non-idiopathic PD were excluded from this study. A flowchart outlining the participant selection process is presented in Figure 1. PD participants were excluded if they met the following criteria: (1) had missing baseline olfactory data (n = 211), (2) did not have idiopathic PD (n = 3), (3) had an H and Y stage >2 (n = 22), or (4) did not have baseline data or at least two subsequent assessments of sleep disorders (EDS and pRBD) during the 5-year follow-up period (n = 113). Ultimately, the study included a total of 589 participants with PD, all of whom had complete baseline datasets and at least two follow-up assessments of sleep disorders.

Fig. 1.

Flowchart of inclusion of the study population.

Fig. 1.

Flowchart of inclusion of the study population.

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Assessment of Olfaction Function

To assess olfactory function among participants with PD, the 40-item University of Pennsylvania Smell Identification Test (UPSIT) was utilized [21]. Each item on the test was scored as 0 or 1, resulting in a summed score ranging from 0 to 40. A higher score on the UPSIT indicated a better status of olfactory function. Consistent with previous research, participants were categorized based on their UPSIT scores, with a score of 34–40 indicating normosmia, 19–33 indicating hyposmia, and lower than 18 indicating anosmia [22].

Assessment of Sleep Disorders

The evaluation of EDS and REM sleep behavior disorder (RBD) involved the use of two separate time-varying exposure variables. EDS was determined using the self-reported Epworth Sleepiness Scale (ESS), an 8-item questionnaire widely used to assess the likelihood of dozing off or falling asleep in various scenarios [23, 24]. Each item on the ESS was scored from 0 to 3, with a maximum total score of 24. A participant scoring 10 or more on the ESS was identified as having EDS, a definition that has been validated in previous studies [23]. pRBD was assessed through the self-rating RBD Screening Questionnaire (RBDSQ), which comprises a 10-item questionnaire about the clinical features of pRBD [25]. Participants with a self-reported RBDSQ score of ≥5 out of a maximum of 13 points were classified as having pRBD. This pRBD definition has demonstrated high sensitivity (0.84) and specificity (0.96) in previous research [25, 26].

Covariates

Demographic and clinical data were collected for all participants. Covariates were adjusted in either a time-varying or time-invariant fashion. For the time-invariant covariates, age at baseline (years), gender (men and women), family history (no or yes), and disease duration (months) were assessed at baseline. For time-varying covariates, they were defined using data from baseline and all 5-year follow-up visits. Demographic variables included age at visit (years) and BMI (body mass index), clinical characteristics included Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III Total Score, Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), and State-Trait Anxiety Inventory-Form Y (STAI-Y).

BMI (kg/m2) is calculated as weight in kilograms divided by height in meters squared. The severity of PD was evaluated by the MDS-UPDRS part III total score [27]. Cognitive function was assessed by MoCA [28]. The 15-item Geriatric Depression Scale (GDS-15) was used as a screening tool for depression [29] and has been commonly studied and validated in young and old PD patients [30]. The STAI-Y was considered to be a valid instrument for assessing anxiety symptoms in individuals with early stage PD [31].

Statistical Analysis

Data were described as median (interquartile range) for continuous variables and as frequency or percentage for categorical variables. Comparison between the two groups was performed via the Kolmogorov-Smirnov test or Mann-Whitney U test for continuous variables and by the χ2 test for categorical variables.

The distinct trajectories of sleep disorders were identified using a group-based trajectory modeling (GBTM) implemented with the Proc Traj procedure in SAS software [32]. This method enabled us to estimate the probabilities for multiple trajectories, rather than modeling a single mean for the study population, which may obscure the differences between groups of individuals [33]. All available ESS scores and RBDSQ scores over 5-year follow-up visits were used to model the longitudinal trajectories of sleep disorders, and the year of data collection was used as the underlying time scale. A censored normal distribution was used to model the successive ESS score and RBDSQ score. The Bayesian information criterion and relative entropy were used to identify the optimal number of trajectories and to determine the best-fitted shape (up to cubic models) of each number of trajectories. Participants were classified into a specific trajectory according to the maximum posterior probability of assignment. An average posterior probability of 0.70 or higher was considered a good fit [32].

We examined the association between olfactory dysfunction and trajectories of sleep disorders using odds ratios (ORs) with 95% confidence intervals (CIs), derived from binomial logistic regression models. Three separate models were fitted for PD patients: (1) adjusted for age at baseline and gender; (2) further adjusted for BMI, family history, GDS score, STAI-Y score, and MoCA score; and (3) additionally adjusted for MDS-UPDRS part III score and disease duration. In addition, the olfactory function was categorized into normosmia, hyposmia, and anosmia according to the UPSIT score, and logistic regression analysis was further performed after controlling for the three separate models mentioned above.

To further investigate the dose-response relationship between olfactory dysfunction and sleep disorders, restricted cubic splines regression allows for the examination of both linear and nonlinear relationships [34]. To balance the optimal fitting and overfitting of the principal spline, the number of knots was determined based on the minimum absolute value of the Akaike information criterion, with a range of three to seven knots selected [35]. Three knots were chosen to correspond to the 10th, 50th, and 90th percentiles.

We also conduct sensitivity analyses. Generalized linear mixed-effects models were used to assess longitudinal associations between these traits over the 5-year follow-up period, with the intercept and slope of age at visit fitted as random effects at the participant level. To test the robustness and potential variations in different subgroups, we conducted an effect modification analysis by population characteristics. We repeated all analyses stratified by age at baseline (<65 or ≥65 years), gender (men or women), and family history (no or years). Potential modifying effects were examined by testing the corresponding multiplicative interaction terms. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) or R software (version 4.1.1). We considered two-sided p values <0.05 to be significant.

Baseline Characteristics of Study Participants

The results presented in Table 1 indicate that among the 589 participants included at baseline, 354 (60.1%) were men, and the median age at baseline was 62.5 years, with an interquartile range of 54.8–68.9 years. When comparing participants with low EDS to those with high EDS, it was observed that high EDS participants were more likely to have a higher prevalence of family history of PD, longer disease duration, higher MDS-UPDRS part III score, higher GDS score, and higher STAI-Y score. Additionally, high EDS participants tended to have lower UPSIT score (all p < 0.05), while no significant differences were observed in other variables (p > 0.05). When comparing participants with low pRBD to those with high pRBD, it was found that high pRBD participants tended to have higher MDS-UPDRS part III score, higher GDS score, and higher STAI score, and exhibited lower UPSIT score (all p < 0.05), while other variables did not show significant differences (p > 0.05).

Table 1.

Baseline characteristics of the participants according to trajectories of sleep disorders

CharacteristicsTotal (n = 589)EDSpRBD
low (n = 415)high (n = 174)p valuelow (n = 424)high (n = 165)p value
Age at baseline, years 62.5 (54.8–68.9) 62.6 (54.9–69.1) 62.4 (54.4–68.7) 0.520 62.8 (55.0–69.4) 62.3 (54.2–68.6) 0.144 
Gender (men) 354 (60.1) 239 (57.6) 115 (66.1) 0.067 245 (57.8) 109 (66.1) 0.080 
Family history (yes) 218 (37.0) 142 (34.2) 76 (43.7) 0.038 153 (36.1) 65 (39.4) 0.514 
BMI 26.4 (23.9–29.6) 26.0 (23.9–29.2) 27.1 (23.9–29.8) 0.110 26.4 (23.7–29.7) 26.6 (24.2–29.4) 0.875 
Disease duration, months 7.1 (3.2–22.5) 6.8 (3.0–20.9) 9.2 (3.5–31.4) 0.021 7.1 (3.3–21.4) 8.1 (3.0–24.0) 0.594 
MDS-UPDRS part III score 18.0 (13.0–25.0) 17.5 (12.8–24.3) 20.0 (14.3–25.0) 0.095 17.0 (13.0–24.0) 20.0 (13.0–26.0) 0.039 
Depression (GDS score) 2.0 (1.0–4.0) 2.0 (0.0–3.0) 2.0 (1.0–4.0) 0.001 2.0 (0.0–3.0) 2.0 (1.0–4.0) 0.001 
Anxiety (STAI score) 63.0 (51.5–78.0) 61.0 (50.0–76.0) 68.0 (56.0–82.0) 0.001 61.0 (50.0–75.0) 69.0 (57.0–83.0) <0.001 
Cognition (MoCA score) 27.0 (25.0–29.0) 27.0 (25.0–29.0) 28.0 (26.0–29.0) 0.110 27.0 (25.0–29.0) 27.0 (26.0–29.0) 0.621 
Olfactory (UPSIT score) 22.0 (16.0–29.0) 22.0 (16.5–29.0) 21.0 (14.0–27.0) 0.009 23.0 (16.0–30.0) 19.0 (15.0–25.0) <0.001 
Olfactory    0.040   0.002 
 Normosmia (34–40 score) 58 (9.8) 47 (11.3) 11 (6.3)  51 (12.0) 7 (4.2)  
 Hyposmia (19–33 score) 341 (57.9) 245 (59.1) 96 (55.2)  25 (59.0) 91 (55.2)  
 Anosmia (<18 score) 190 (32.3) 123 (29.6) 67 (38.5)  123 (29.0) 67 (40.6)  
CharacteristicsTotal (n = 589)EDSpRBD
low (n = 415)high (n = 174)p valuelow (n = 424)high (n = 165)p value
Age at baseline, years 62.5 (54.8–68.9) 62.6 (54.9–69.1) 62.4 (54.4–68.7) 0.520 62.8 (55.0–69.4) 62.3 (54.2–68.6) 0.144 
Gender (men) 354 (60.1) 239 (57.6) 115 (66.1) 0.067 245 (57.8) 109 (66.1) 0.080 
Family history (yes) 218 (37.0) 142 (34.2) 76 (43.7) 0.038 153 (36.1) 65 (39.4) 0.514 
BMI 26.4 (23.9–29.6) 26.0 (23.9–29.2) 27.1 (23.9–29.8) 0.110 26.4 (23.7–29.7) 26.6 (24.2–29.4) 0.875 
Disease duration, months 7.1 (3.2–22.5) 6.8 (3.0–20.9) 9.2 (3.5–31.4) 0.021 7.1 (3.3–21.4) 8.1 (3.0–24.0) 0.594 
MDS-UPDRS part III score 18.0 (13.0–25.0) 17.5 (12.8–24.3) 20.0 (14.3–25.0) 0.095 17.0 (13.0–24.0) 20.0 (13.0–26.0) 0.039 
Depression (GDS score) 2.0 (1.0–4.0) 2.0 (0.0–3.0) 2.0 (1.0–4.0) 0.001 2.0 (0.0–3.0) 2.0 (1.0–4.0) 0.001 
Anxiety (STAI score) 63.0 (51.5–78.0) 61.0 (50.0–76.0) 68.0 (56.0–82.0) 0.001 61.0 (50.0–75.0) 69.0 (57.0–83.0) <0.001 
Cognition (MoCA score) 27.0 (25.0–29.0) 27.0 (25.0–29.0) 28.0 (26.0–29.0) 0.110 27.0 (25.0–29.0) 27.0 (26.0–29.0) 0.621 
Olfactory (UPSIT score) 22.0 (16.0–29.0) 22.0 (16.5–29.0) 21.0 (14.0–27.0) 0.009 23.0 (16.0–30.0) 19.0 (15.0–25.0) <0.001 
Olfactory    0.040   0.002 
 Normosmia (34–40 score) 58 (9.8) 47 (11.3) 11 (6.3)  51 (12.0) 7 (4.2)  
 Hyposmia (19–33 score) 341 (57.9) 245 (59.1) 96 (55.2)  25 (59.0) 91 (55.2)  
 Anosmia (<18 score) 190 (32.3) 123 (29.6) 67 (38.5)  123 (29.0) 67 (40.6)  

MDS-UPDRS Part III, Movement Disorders Society-Unified Parkinson Disease Rating Scale Part III; GDS, Geriatric Depression Scale; STAI-Y, State-Trait Anxiety Inventory-Form Y; MoCA, Montreal Cognitive Assessment; UPSIT, University of Pennsylvania Smell Identification Test; EDS, excessive daytime sleepiness; ESS, Epworth Sleepiness Scale; pRBD, probable rapid eye movement sleep behavior disorder; RBDSQ, rapid eye movement sleep behavior disorder screening questionnaire.

Trajectories of Sleep Disorders

We tested models with one to four trajectories and found that the model with two trajectories had the lowest relative entropy, with average posterior probabilities greater than 0.70 for all trajectory groups (online suppl. Tables S1, S2; for all online suppl. material, see https://doi.org/10.1159/000539330). Therefore, two distinct trajectories of EDS and pRBD were identified over the 5-year follow-up period. For EDS, the two distinct trajectories were characterized by maintaining a low EDS score, which was observed in 415 participants (70.5%), and maintaining a high EDS score, which was seen in 174 participants (29.5%). Similarly, for pRBD, two distinct trajectories were defined as maintaining a low pRBD score, observed in 424 participants (72.0%), and maintaining a high pRBD score, seen in 165 participants (28.0%) as shown in Figure 2.

Fig. 2.

Trajectories of sleep disorders over 5-year follow-up visit in Parkinson’s disease patients.

Fig. 2.

Trajectories of sleep disorders over 5-year follow-up visit in Parkinson’s disease patients.

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Association between Olfactory Dysfunction and Trajectories of Sleep Disorders

In the binomial logistic regression analysis (Table 2), an inverse association pattern was revealed between olfactory function measures and trajectories of EDS, with an OR of 0.97 and a 95% CI of 0.95–1.00 (p = 0.038), after controlling for all potential covariates. Likewise, olfactory function was significantly associated with lower trajectories of pRBD, with an OR of 0.96 and a 95% CI of 0.94–0.98 (p = 0.001) among individuals with early PD. Similar results were also observed for the analyses of other models.

Table 2.

Binomial logistic regression analysis for the association between olfactory dysfunction and trajectories of sleep disorders

EDSpRBD
OR95% CIp valuedOR(95% CI)p valued
Crude 0.97 (0.95, 0.99) 0.005 0.96 (0.93, 0.98) <0.001 
Model 1a 0.97 (0.95, 0.99) 0.005 0.95 (0.93, 0.97) <0.001 
Model 2b 0.97 (0.95, 0.99) 0.009 0.95 (0.93, 0.98) <0.001 
Model 3c 0.97 (0.95, 1.00) 0.038 0.96 (0.94, 0.98) 0.001 
EDSpRBD
OR95% CIp valuedOR(95% CI)p valued
Crude 0.97 (0.95, 0.99) 0.005 0.96 (0.93, 0.98) <0.001 
Model 1a 0.97 (0.95, 0.99) 0.005 0.95 (0.93, 0.97) <0.001 
Model 2b 0.97 (0.95, 0.99) 0.009 0.95 (0.93, 0.98) <0.001 
Model 3c 0.97 (0.95, 1.00) 0.038 0.96 (0.94, 0.98) 0.001 

EDS, excessive daytime sleepiness; pRBD, probable rapid eye movement sleep behavior disorder; BMI, body mass index; GDS, Geriatric Depression Scale; STAI-Y, State-Trait Anxiety Inventory-Form Y; MoCA, Montreal Cognitive Assessment; OR, odds ratio; CI, confidence interval.

aModel 1: adjusted for age and gender.

bModel 2: additionally adjusted for BMI, family history, GDS score, STAI-Y score, and MoCA score.

cModel 3: additionally adjusted for Movement Disorders Society-Unified Parkinson Disease Rating Scale Part III Total Score, disease duration.

dp value <0.05 (two-sided).

When the categorical olfactory score was used as the exposure (Table 3), compared to participants with normosmia, the OR of EDS was 1.66 (95% CI, 0.85–3.50) for hyposmia and 2.31 (95% CI, 1.14–5.01) for anosmia after multivariable adjustment (p = 0.016 for trend). Similarly, compared to participants with normosmia, the OR of pRBD was 2.68 (95% CI, 1.24–6.70) in hyposmia and 4.19 (95% CI, 1.88–10.71) in anosmia (p < 0.001 for trend).

Table 3.

Associations between olfactory dysfunction (categorical) and sleep disorders

Cases, nEDSpRBD
OR(95% CI)p valueOR95% CIp value
Olfactory 
 Normosmia 58 1.00   1.00   
 Hyposmia 341 1.66 (0.85, 3.50) 0.158 2.68 (1.24, 6.70) 0.020 
 Anosmia 190 2.31 (1.14, 5.01) 0.026 4.19 (1.88, 10.71) 0.001 
p trend    0.016   <0.001 
Cases, nEDSpRBD
OR(95% CI)p valueOR95% CIp value
Olfactory 
 Normosmia 58 1.00   1.00   
 Hyposmia 341 1.66 (0.85, 3.50) 0.158 2.68 (1.24, 6.70) 0.020 
 Anosmia 190 2.31 (1.14, 5.01) 0.026 4.19 (1.88, 10.71) 0.001 
p trend    0.016   <0.001 

EDS, excessive daytime sleepiness; pRBD, probable rapid eye movement sleep behavior disorder; OR, odds ratio; CI, confidence interval.

Adjusted for age and gender.

p value <0.05 (two-sided).

Dose-Response Relationship between Olfactory Dysfunction and Sleep Disorder

To illustrate the nonlinear associations between olfactory dysfunction and the odds of sleep disorders, the adjusted models with restricted cubic splines depicted the dose-response relationship between olfactory dysfunction and sleep disorders (Fig. 3). A statistically significant nonlinear association was observed in olfactory dysfunction with pRBD (p < 0.05 for nonlinear), whereas not found in EDS (p > 0.05 for nonlinear).

Fig. 3.

Dose-response association of olfactory dysfunction with sleep disorder.

Fig. 3.

Dose-response association of olfactory dysfunction with sleep disorder.

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Sensitivity and Subgroup Analyses

To further conduct a linear mixed model to examine the association between baseline olfactory dysfunction and EDS and pRBD over time (online suppl. Table S3), an inverse association of olfactory function with odds of pRBD was observed in all models. However, olfactory function was associated with lower levels of EDS in anosmia subgroup (β = −0.04, 95% CI −0.08, −0.01, p = 0.026). The association between olfactory dysfunction and trajectories of EDS substantially differed by age at baseline (<65 and ≥65 years) (p = 0.013 for interaction) in online supplementary Table S4 and Figure S1. Similarly, a significant interaction was found between gender (men and women) and olfactory dysfunction with pRBD (p = 0.007 for interaction). However, no significant interactions were found between PD family history and olfactory dysfunction with EDS and pRBD after correcting for multiple tests.

To our knowledge, this study is the first to investigate the association between olfactory dysfunction and the trajectories of EDS and pRBD in patients with PD. Based on the longitudinal cohort design with repeated measurements, we identified two distinct trajectories of sleep disorders characterized during a 5-year follow-up. Our findings suggested that participants with olfactory dysfunction were at increased likelihood of experiencing worsening EDS and pRBD.

Our findings indicated that low trajectory of sleep disorders exhibited a relatively low baseline and slight development over the follow-up period, while high trajectory displayed a relatively high baseline and distinct development, implying potential distinctions in the underlying causes of sleep disorders trajectories. Our results regarding two trajectories of sleep disorders were similar to the findings of previous studies, revealing substantial differences in trajectory patterns [36‒40]. Specifically, evidence has demonstrated three trajectories of daytime sleepiness: no, emerging, and persistent [36]. In fact, the etiology of EDS in PD is known to be multifactorial, involving factors such as disease stage and duration, dopaminergic drugs, and other sleep disorders including insomnia, poor nocturnal sleep, and parasomnia [41‒44]. However, EDS in PD may be directly associated with the impairment of the circadian rhythm sleep-wake modulatory regions, possibly owing to the use of dopaminergic agents and neurodegeneration [41, 45]. Notedly, EDS could occur independently of nocturnal sleep disturbances, potentially due to neurodegeneration affecting specific nuclei regulating sleep-wake structures [44].

Non-motor symptoms, such as olfactory dysfunction and RBD, are well-established prodromal symptoms of PD, often characterizing the early stages of alpha-synucleinopathies [46]. When isolated RBD is accompanied by hyposmia, there is an elevated probability of phenoconversion to PD or dementia with Lewy bodies [47]. Our findings demonstrated that olfactory dysfunction was substantially associated with unfavorable trajectories of EDS and pRBD among PD patients. To date, no study has investigated the association between olfactory dysfunction and the trajectories of EDS and pRBD in patients with PD. Several epidemiological studies have examined the association between olfactory dysfunction and RBD in PD patients [15, 48, 49], whilst not observed in EDS. Based on a prior meta-analysis including 28 studies with 2,858 participants, olfactory dysfunction could be recognized as a reliable diagnostic biomarker of RBD, which appears to identify early disease conversion from idiopathic RBD to PD [50].

The relationship between olfactory dysfunction and the risk of sleep disorders in PD is not fully understood at a mechanistic level. Sleep patterns have been shown to affect odor sensitivity, with a decline in perceptible odors observed during certain sleep stages [51]. Conversely, odors have the potential to influence sleep by modulating factors such as arousal levels, sleep onset latency, duration, and overall quality [51]. Notably, specific neuronal populations within regions such as the basal forebrain, axial nucleus, and locus coeruleus play critical roles in both the olfactory system and the regulation of sleep-wake cycles [51]. Studies utilizing Drosophila models have demonstrated that the activation of distinct neurons within the olfactory pathway can exert bidirectional effects on sleep modulation [52]. According to the Braak staging, abnormalities in the olfactory bulb are commonly observed in early stage PD [53, 54]. This suggests that olfactory dysfunction is closely associated with the deposition of Lewy bodies, a characteristic feature of PD pathology, which outlines the spread of Lewy body pathology from the caudal (lower) to the rostral (upper) regions of the brain [53, 55]. Additionally, atrophy of central olfactory structures, particularly the olfactory cortex, gyrus rectus, and amygdala, may serve as potential indicators of premotor synucleinopathy in idiopathic RBD [56]. Research in these areas, including studies on neurobiology, genetics, and neuroimaging, is ongoing and may further elucidate the biological mechanisms underlying the relationship between olfactory function and sleep disorders. Understanding these mechanisms could have implications for the early detection and management of both olfactory dysfunction and sleep disturbances in various neurological conditions.

This study has several notable strengths. First, it is the first to examine the association between olfactory dysfunction and the trajectories of EDS and pRBD in patients with PD. This represents a significant contribution to the understanding of sleep disorders in PD. Second, the 5-year follow-up period allowed for the evaluation of long-term dynamic EDS and pRBD based on multiple repeated measurements, providing valuable insights into the trajectory patterns of these sleep disorders in PD patients. Lastly, the use of group-based trajectory modeling enabled the simultaneous estimation of probabilities for multiple trajectories, offering a more comprehensive understanding of the sleep disorder trajectories in the study population [33]. However, the study also has some limitations. First, olfactory dysfunction and sleep disorders were evaluated based on self-reported scales (i.e., UPSIT, ESS, RBDSQ), which may introduce information bias. Nonetheless, these scales have demonstrated fair validity and reliability among PD patients. Moreover, the Parkinson’s Disease Sleep Scale-2 (PDSS-2) is a well-established and validated tool that offers a comprehensive evaluation of sleep problems in individuals with PD [57]. The absence of PDSS-2 data in the PPMI database hindered its inclusion and evaluation in our study. Further studies could incorporate PDSS-2 to better classify sleep disorders in patients with PD. Second, despite adjusting for a wide range of potential confounders in the analysis, the possibility of residual confounding cannot be completely ruled out. Further, EDS observed in PD may have a complex underlying cause that involves multiple factors. These factors could include changes in the physiological processes that regulate sleep and wakefulness, potential side effects of dopaminergic therapy commonly used to manage PD symptoms, issues related to poor quality of nocturnal sleep, medications, or treatments that the individual may be receiving. Because only a few patients received dopaminergic therapy in our study sample, additional analysis taking into account these factors cannot be performed. Lastly, the loss of some participants during follow-up may affect the reliability of the results to a certain extent. Therefore, to enhance the accuracy and validity of the findings, future research should consider expanding the sample size and extending the follow-up period. This would provide a more robust and comprehensive understanding of the association between olfactory dysfunction and sleep disorder trajectories in PD patients.

Our findings indicated that there was an association between olfactory dysfunction and the development of sleep disorders in individuals with early PD. This suggested that olfactory dysfunction may serve as a potential indicator or contributor to the progression of sleep disorders in patients with PD. It becomes crucial for healthcare professionals and caregivers to pay close attention to both olfactory and sleep-related issues when managing PD. By addressing these conditions early on and implementing appropriate interventions, such as targeted treatments or support, it may be possible to improve the overall well-being and management of PD for affected individuals. This underscores the significance of a comprehensive approach to care that encompasses both olfactory and sleep-related concerns in individuals with PD.

PPMI (a public-private partnership) was funded by the Michael J. Fox Foundation for Parkinson’s Research and multiple funding partners, whose names can be found at www.ppmi-info.org/fundingpartners.

The data were obtained from the PPMI database, which contains de-identified clinical data. This study was conducted in line with the Declaration of Helsinki and the Good Clinical Practice (GCP) guidelines, following approval from the Local Ethics Committees at each participating site.

The authors have no conflicts of interest to declare.

This study received support from Sun Yat-Sen University and the Pearl River Scholar Program of Guangdong Province (Health Science Section, No. 0920220206).

E.T. and Y.Z. designed and conceptualized this study; Q.J. and Q.L. extracted the data and performed statistical analysis; Y.X., E.T., and Y.Z. conducted the study and critiqued the manuscript; M.Y. drafted the initial version of the manuscript. All authors have reviewed and approved the final version of this manuscript.

The data supporting the findings of this article were available and can be downloaded from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmiinfo.org/data). For up-to-date information on the study, visit www.ppmiinfo.org.

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