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Longitudinal decline in DAT binding in Parkinson’s disease: connections with sleep disturbances

BMC Medicine volume 22, Article number: 550 (2024) Cite this article

Abstract

Background

The nigrostriatal dopamine (DA) system plays a critical role in regulating the sleep–wake state. The relationship between baseline striatal DA transporter (DAT) specific binding ratios (SBR) and rapid eye movement sleep behavior disorder (RBD) has been established. This study aimed to investigate the association between the progression of striatal DA dysfunction and sleep disturbances, including excessive daytime sleepiness (EDS) and probable RBD (pRBD), in patients with Parkinson’s disease (PD).

Methods

Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI). Six hundred twenty-one newly diagnosed PD patients and followed up for 4 years were included in this longitudinal study. EDS and pRBD were defined using the Epworth Sleepiness Scale (ESS) and RBD Screening Questionnaire (RBDSQ). Striatal DAT SBR was evaluated by [123I] FP-CIT SPECT.

Results

Using a linear mixed-effects model across all contemporaneous data points, we found a negative correlation between striatal DAT SBR and sleep disturbances (EDS/pRBD). The interaction between striatal DAT SBR and year was specific to RBDSQ score (β = − 0.102, 95% CI: − 0.187 to − 0.017, p = 0.019), with no evidence of a similar interaction for ESS score. Additionally, the association between the alpha-synuclein gene (SNCA) and sleep disturbances was mediated by the SBR (ESS score: total effect = − 2.717, p = 0.022; direct effect = − 3.222, p = 0.007; indirect effect = 0.505, p < 0.05; RBDSQ score: total effect = 1.402, p = 0.026; direct effect = 1.209, p = 0.057; indirect effect = 0.193, p < 0.05).

Conclusions

Our findings support the role of striatal DA dysfunction in sleep disturbances in early PD patients. Furthermore, we demonstrated that genetic variations in causative genes of PD contribute to the development of sleep disturbances. Striatal DAT imaging may be a useful risk indicator for sleep disturbances, providing early intervention strategies.

Peer Review reports

What is already known on this topic

Sleep disturbances, including excessive daytime sleepiness (EDS) and rapid eye movement sleep behavior disorder (RBD), are the most prevalent non-motor symptoms of Parkinson’s disease (PD). The nigrostriatal dopamine (DA) system plays a critical role in regulating sleep–wake cycles. While a link between baseline striatal DA transporter (DAT) specific binding ratios (SBR) and RBD has been established, longitudinal studies investigating this association remain scarce.

What this study adds

By employing repeated DAT imaging over a four-year period in this longitudinal cohort of PD patients, we observed a negative correlation between striatal DAT SBR and sleep disturbances (both EDS and pRBD). This association exhibited a progressive strengthening effect, specifically in patients with pRBD. Additionally, our findings suggest that striatal DAT SBR has a significant mediating effect on the association between alpha-synuclein gene (SNCA) mutation and sleep disturbances.

How this study might affect research, practice or policy

Our research suggests that DAT imaging is a potential imaging biomarker for sleep disturbances in PD patients.

Background

Sleep disturbances encompass the most common non-motor symptoms of Parkinson’s disease (PD), including insomnia, excessive daytime sleepiness (EDS), and rapid eye movement sleep behavior disorder (RBD). These disturbances significantly impact patients’ psychological, vocational, and social functions as well as personal safety [1, 2]. EDS is characterized by difficulty staying awake and alert during the day, resulting in unconscious sleep episodes or a sleepy state [3]. RBD is an abnormal sleep state characterized by recurrent sleep-related abnormal vocalizations or complex motor behaviors during rapid eye movement sleep [4]. The presence of RBD in PD patients is often associated with a poorer prognosis in later life, including higher risks for severe motor complications, cognitive dysfunction, and autonomic dysfunction [2, 5].

Recent investigations into the biological underpinnings of sleep disturbances have shed light on the critical role of neurotransmitter systems in regulating sleep–wake cycles. The substantia nigra dopamine (DA) system is particularly important for behavioral arousal, while norepinephrine and acetylcholine are more closely associated with electroencephalographic arousal. Furthermore, DA has been demonstrated to play a vital role in maintaining wakefulness and regulating sleep homeostasis [6]. In PD, the loss of dopaminergic neurons in the nigrostriatal pathway of the midbrain disrupts the balance of neurotransmitter projections to the brain’s ascending reticular activating system, potentially leading to sleep disturbances experienced by PD patients.

PD can be evaluated in vivo using [123I] FP-CIT single-photon emission computed tomography (SPECT), a technique that measures striatal DA transporter (DAT) density, a marker of dopaminergic neurodegeneration [7, 8]. While a prior longitudinal study identified a negative correlation between striatal DAT and sleep disturbances in early PD [9], it focused on general non-motor symptoms and did not explore specific symptom relationships. Additionally, smaller cross-sectional studies suggest an association between RBD and striatal DAT depletion [10, 11]. Given that sleep disturbances can precede PD diagnosis by years and are easily assessed, they hold potential as prodromal PD markers. However, previous studies linking DAT with sleep disturbances were limited to single time points without continuous follow-up [11,12,13]. To address this gap, we utilize data from DAT-SPECT imaging in the Parkinson’s Progression Markers Initiative (PPMI) database to investigate the longitudinal relationship between DAT binding and the development and progression of sleep disturbances in PD patients.

Methods

Study participants

Data were obtained from the PPMI database, a longitudinal, observational, international multicenter research study (https://www.ppmi-info.org/) [14]. Launched in 2010 to identify biomarkers for PD onset and progression, PPMI collects and evaluates clinical, imaging, and genetic data from PD patients. The study enrolled participants with early, untreated PD alongside healthy controls matched for age and sex. This recruitment process occurred between March 2010 and August 2022, and all participants provided written informed consent prior to study initiation. Notably, the study is registered with ClinicalTrials.gov (NCT01141023).

Our analysis focused on PD participants within the PPMI database who had completed 4 years of follow-up. We downloaded baseline assessments and subsequent annual follow-up information for these participants. Participants with missing follow-up data, such as sleep disturbance rating scales or [123I] FP-CIT SPECT examinations, were excluded from the analysis.

Sleep disturbance assessments

This study assessed sleep disturbances, including EDS and probable RBD (pRBD). Based on the Epworth Sleepiness Scale (ESS) score (≥ 10 indicating EDS and < 10 indicating non-EDS) [15], we categorized participants into EDS and non-EDS subgroups. Additionally, the presence of pRBD symptoms was evaluated by the RBD Screening Questionnaire (RBDSQ) [16], with a score greater than 5 considered positive for pRBD [17]. However, since polysomnography was not used to confirm these cases, we employed the term “probable RBD” throughout the study.

Dopaminergic imaging

All PD participants underwent [123I] FP-CIT SPECT scans at baseline and 1, 2, and 4 years. Following the established protocol (http://www.ppmi-info.org/study-design/research-documents-and-sops/), these scans were acquired at the PPMI Imaging Centers and subsequently transferred to the Imaging Core for data processing and striatal specific binding ratio (SBR) calculation. The SBR, a summative measure of caudate and putamen SBR, was calculated using the formula: (target region/reference region) − 1.

Statistical analysis

Statistical analyses were performed using SPSS Statistics version 25.0 (IBM Corp., Armonk, NY), with GraphPad Prism version 9 (GraphPad Software, San Diego, CA) used for generating data visualizations. A two-tailed p-value of less than 0.05 was considered statistically significant.

The normality of the data was assessed using the Kolmogorov–Smirnov test. Normally distributed continuous variables were expressed as mean ± standard deviation. Conversely, continuous variables that were not normally distributed were expressed as median (interquartile range). Categorical variables were presented as percentages. Based on the results of the normality tests, the Kruskal–Wallis test, Mann–Whitney U test, and chi-square tests were employed as appropriate.

To investigate the association between sleep disturbances and striatal DAT SBR over time, a linear mixed-effects model was employed. This model examined ESS/RBDSQ scores (dependent variables) and striatal DAT SBR (independent variable) collected at each time point. Including an interaction term between SBR and time (defined as the year of follow-up assessment) facilitated the use of all available data to assess how the relationship between sleep disturbances and SBR changed with disease progression. After observing a significant interaction effect, Pearson correlation analysis was conducted to verify the correlation between striatal DAT SBR and sleep disturbances at each time point separately. Additionally, a multivariate logistic regression model was employed to further explore the relationship between striatal DAT SBR and sleep disturbances. Moreover, to investigate the potential mediating role of striatal DAT SBR on the association between genetic mutations [alpha-synuclein gene (SNCA), leucine-rich repeat kinase 2 gene (LRRK2), and glucocerebrosidase gene (GBA)] and sleep disturbances, mediation analysis was performed using the PROCESS macro for SPSS.

Results

Characteristics of participants

A total of 621 PD patients with baseline clinical and imaging data were included in this study. Participant characteristics are summarized in Table 1. Due to the mixed-effects modeling approach, data from all participants who underwent [123I] FP-CIT SPECT imaging at any follow-up time point (year 1: n = 328, 52.8%; year 2: n = 287, 46.2%; year 4: n = 238, 38.3%) were incorporated into the analysis. This approach allowed us to maximize the available data and investigate changes over time.

Table 1 Characteristics of PPMI participants with Parkinson’s disease (PD)
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Baseline demographics and clinical characteristics of the PD patients revealed that 60.0% were male, with a median age of 62.9 years and 16 years of education. Additionally, 18.1% of patients were classified with EDS and 23.1% with pRBD. As expected, striatal DAT SBR in PD patients was approximately half that of healthy controls (p < 0.001). Furthermore, our findings indicated a progressive decline in striatal DAT SBR over time (Kruskal–Wallis test: p < 0.001; Fig. 1A).

Fig. 1
figure 1

A Violin plots showing the distribution of striatal dopamine transporter (DAT) specific binding ratio (SBR) in Parkinson’s disease (PD, colored plots) over time relative to baseline (BL), compared with healthy controls (HC, blue plot) at BL. B Violin plots showing striatal DAT SBR at different sleep disturbances groups, across all years

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Relationship between striatal DAT SBR and sleep disturbances

Striatal DAT SBR was significantly lower in PD patients with sleep disturbances (EDS and pRBD) than those without (Fig. 1B). Longitudinal analysis revealed significant differences in the overall association between striatal DAT SBR and both ESS and RBDSQ scores across all time points (β = − 0.480, 95% CI: − 0.816 to − 0.144, p = 0.005 for ESS; β = − 0.239, 95% CI: − 0.433 to − 0.044, p = 0.016 for RBDSQ, Fig. 2A, B). Notably, in the model adjusted for RBDSQ score [including demographics, levodopa-equivalent daily dose (LEDD), motor symptoms, cognition and depression], a significant interaction effect was observed between striatal DAT SBR and time (β = − 0.102, 95% CI: − 0.187 to − 0.017, p = 0.019) but not for ESS score. Consistent findings were obtained using multivariate logistic regression analysis (Fig. 2C, D). Specifically for patients with pRBD, a significant interaction effect between striatal DAT SBR and time was identified [odds ratio (OR): 0.873, 95% CI: 0.775 to 0.984, p = 0.026, Fig. 2D]. Post hoc analyses revealed a negative correlation between striatal DAT SBR and RBDSQ score at baseline (r = − 0.126, p = 0.002), which strengthened progressively over time (− 0.126 vs. − 0.157 vs. − 0.201 vs. − 0.290, Fig. 3A–D). This suggests a progressively weakening dopaminergic function (decreasing striatal DAT SBR) coinciding with a worsening of RBD symptoms (increasing RBDSQ score) as the disease progresses. In contrast, no such progressive strengthening of the association was observed between striatal DAT SBR and ESS score, although a negative correlation was present at baseline, year 1, and year 4 (Fig. 3E–H). Separate analyses of the caudate and putamen yielded similar results, demonstrating negative associations with both sleep disturbances scores (Additional file 1: Fig. S1). Notably, the putamen also exhibited a significant interaction effect with time for RBDSQ score (β = − 0.403, 95% CI: − 0.664 to − 0.142, p = 0.003, Additional file 1: Fig. S1D).

Fig. 2
figure 2

Statistical analysis results of the striatal dopamine transporter (DAT) specific binding ratio (SBR). Linear mixed effects model investigating the relationship between striatal DAT SBR and sleep disturbances score. A for Epworth Sleepiness Scale (ESS) score; B for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score. Multivariate logistic regression analysis investigating the relationship between striatal DAT SBR and sleep disturbances. C for excessive daytime sleepiness (EDS); D for probable rapid eye movement sleep behavior disorder (pRBD). Points represent estimated regression coefficients and bars represent 95% CIs; *p < 0.05; **p < 0.01; ***p < 0.001

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Fig. 3
figure 3

Scatter plots displaying unadjusted linear regressions between striatal dopamine transporter (DAT) specific binding ratio (SBR) and sleep disturbances score at years 0 (baseline), 1, 2, and 4. A–D for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score. E–H for Epworth Sleepiness Scale (ESS) score

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To investigate the longitudinal association between striatal DAT SBR and sleep disturbances, we restricted the analysis to post-baseline time points and included baseline striatal DAT SBR as a covariate in the adjusted model (Additional file 1: Fig. S2). Separate analyses explored the relationship between changes in DAT SBR from baseline and sleep disturbance scores. These analyses revealed no significant association between changes in DAT SBR and sleep disturbances (Additional file 1: Fig. S3). Interestingly, we observed a progressively stronger correlation between absolute striatal DAT SBR and RBDSQ scores as PD progressed (Additional file 1: Fig. S3B). These findings suggest that absolute striatal DAT SBR, rather than the change from baseline is more directly related to RBDSQ scores.

Striatal DAT SBR longitudinally mediates the relationship between different mutations and sleep disturbances

The Kruskal–Wallis test revealed significant differences in sleep disturbances and striatal DAT SBR among the GBA-PD, LRRK2-PD, and SNCA-PD subgroups (p = 0.040, p < 0.001, and p = 0.009, respectively; Table 2). Furthermore, mediation analyses demonstrated a direct association between the SNCA mutation and lower ESS score (β = −3.222, p = 0.007). This association was partially mediated by striatal DAT SBR (β = 0.505, p < 0.05, Fig. 4A). While the SNCA mutation was not directly linked to RBDSQ score, it appeared to influence it indirectly through striatal DAT SBR (β = 0.193, p < 0.05, Fig. 4B). For the GBA mutation, no direct or indirect effect on ESS score was observed (Additional file 1: Fig. S4A). However, it did exert a direct effect on RBDSQ score (β = 1.000, p = 0.001, Additional file 1: Fig. S4B). Finally, no significant mediating effect of striatal DAT SBR was found between LRRK2 mutation and sleep disturbances (Additional file 1: Figs. S5A, B).

Table 2 Baseline characteristics of the GBA-PD, LRRK2-PD, and SNCA-PD group
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Fig. 4
figure 4

Mediation analysis of SNCA mutation for sleep disturbances scores through striatal dopamine transporter (DAT) specific binding ratio (SBR). A for Epworth Sleepiness Scale (ESS) score; B for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score

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Discussion

This study employed a longitudinal design to investigate the association between striatal dopaminergic dysfunction and sleep disturbances in PD patients. Utilizing [123I] FP-CIT SPECT, a neuroimaging marker commonly employed in clinical practice, we observed a negative correlation between striatal DAT SBR and both EDS and pRBD. Notably, this association exhibited a progressive strengthening effect in patients with pRBD. Additionally, our findings revealed a direct association between SNCA gene mutation and lower ESS scores. Furthermore, this association with ESS scores was partially mediated by striatal DAT SBR. The SNCA mutation also appeared to indirectly influence RBDSQ scores by mediating striatal DAT SBR.

Our findings add to the existing body of research by demonstrating a strong correlation between striatal dopaminergic dysfunction and sleep disturbances in PD. This suggests that striatal DAT imaging using [123I] FP-CIT SPECT, a technique already employed in clinical practice, may be a valuable tool for identifying PD patients at risk of developing sleep disturbances. According to the diagnostic criteria for PD [18], normal functional neuroimaging examination of the presynaptic dopaminergic system is one of the definitive exclusion criteria for PD. Therefore, some patients will undergo presynaptic dopaminergic system functional neuroimaging examinations in order to diagnose and differentiate PD. Although PD patients do not typically receive presynaptic dopaminergic system functional neuroimaging to evaluate nigrostriatal dopamine levels for predicting sleep disturbances, nigrostriatal dopamine levels may serve as an early warning indicator of potential sleep disturbances in PD patients who have already undergone this examination. Although there are no medications to prevent sleep disturbances, early intervention measures can be taken for this group of patients, such as cultivating good sleep habits and developing a regular sleep schedule to reduce the impact of sleep disturbances on their daily lives. However, it is important to acknowledge that this study utilized data from a single database, PPMI. Further validation across multiple centers is necessary to establish striatal DAT SBR as a reliable imaging marker for sleep disturbances in PD.

Interestingly, the progressively strengthening association between striatal DAT SBR and RBDSQ score was observed in both putamen and caudate subregions. The striatum can be further segmented into ventral and dorsal regions based on their dopaminergic innervation. The ventral striatum receives DA from the ventral tegmental area (VTA), while the dorsal striatum receives DA from the substantia nigra pars compacta (SNc) [19]. Research suggests that the VTA undergoes later and less severe degeneration in PD compared to the SNc [20, 21]. Kawk et al. proposed that RBD in early-stage PD patients might be linked to presynaptic dopaminergic denervation in the ventral striatum and anterior caudate [22]. Based on these findings, we hypothesize that the observed time-dependent interaction could be attributed to the later dopaminergic denervation of the ventral striatum, with RBD being less affected by the earlier degeneration in the caudate, which is more involved in motor function.

Expanding on previous research, genetic variants in PD causative genes may contribute to the development of sleep disturbances. A systematic review and meta-analysis by Huang et al. demonstrated that GBA variants increased the risk and severity of RBD in PD patients, whereas the LRRK2 G2019S variant decreased this risk and severity [23]. Notably, their study also found that neither GBA nor LRRK2 variants affected the risk of EDS in PD. Furthermore, the influence of GBA variants on RBD risk appears to depend on the variant severity [23]. This finding is particularly interesting given the established role of RBD as a prodromal marker for α-synucleinopathy and the close association between the GBA gene and α-synuclein. Impaired proteolysis caused by glucocerebrosidase (GCase) deficiency in GBA mutations is thought to preferentially increase α-synuclein deposition and its pathological spread, while elevated α-synuclein levels further decrease GCase activity [24]. Consistent with this notion, lower levels of α-synuclein in cerebrospinal fluid (CSF) were observed in PD patients carrying GBA variants, compared to idiopathic PD (iPD) patients, with a trend of decreasing levels across severe, mild, and risk variants [25]. These differences in α-synuclein expression among GBA variants may contribute to phenotypic discrepancies. The underlying mechanism for the low frequency of RBD in LRRK2-PD remains under investigation. Prior studies have shown a lower risk of dementia in LRRK2 patients compared to iPD [26,27,28]. Given the strong association of RBD with dementia in iPD and its link to cholinergic loss [29], Ehrminger et al. [30] speculated that the absence of RBD and rare dementia in LRRK2 PD patients might share a similar pathophysiological basis, such as potentially lower loss of cholinergic neurons. Interestingly, patients carrying the LRRK2 G2019S variant displayed more Lewy body pathology compared to those with other LRRK2 variants [28]. Skin biopsy studies revealed no difference in phosphorylated α-synuclein deposition between LRRK2 G2385R carriers and non-carriers [31], while high levels of α-synuclein deposition were detected in sympathetic noradrenergic nerves in the skin of patients with LRRK2 G2019S and R1441G variants [32]. Our study corroborates these findings, demonstrating a direct effect of GBA mutations on RBDSQ score (β = 1.000, p = 0.001) and a similar direct effect for LRRK2 (β = − 1.191, p < 0.001). Importantly, for the ESS score, no significant mediation effect was observed for either GBA or the LRRK2 mutations (both total effects > 0.05).

Mediation analyses revealed a direct association between SNCA mutations and lower ESS scores (β = − 3.222, p = 0.007), with this association partially mediated by striatal DAT SBR (β = 0.505, p < 0.05). Interestingly, SNCA mutations were not directly linked to RBDSQ scores but appeared to indirectly influence them through striatal DAT SBR (β = 0.193, p < 0.05). SNCA encodes α-synuclein proteins, which accumulate abnormally in PD as the main component of Lewy bodies. A recent study employed comprehensive single nucleotide polymorphism (SNP) genotyping and full SNCA sequencing to identify RBD-specific risk variants. They identified a 5′-region SNCA variant (rs10005233) associated with idiopathic RBD that replicated in PD patients with pRBD [33]. In addition, a study reported negative correlations between the rs3910105 genotype and putamenal DAT availability with SNCA transcripts, suggesting this SNP might influence PD progression [34]. However, Koros et al. found no significant differences in sleep disturbances (RBD or EDS) between patients with and without the SNCA p.A53T variant [35]. These findings highlight the ongoing debate regarding the role of SNCA mutations in sleep disturbances, emphasizing the need for further validation through larger, longitudinal studies.

Given that the question of whether α-synuclein causes degeneration or is a product of the degeneration process remains still unclear, the underlying cause of PD requires further investigation and discussion. In the study by Hu et al. [36], there was a significant correlation between tau levels in CSF and clinical manifestations in PD patients. Furthermore, several in vitro and in vivo studies have also explored the role of tau in PD [37, 38]. The speculations regarding on how tau may participate in or affects the pathophysiology of PD are as follows: axonal transport dysfunction may contribute to α-synuclein deposition and impair the excretion of metabolites [39, 40]; tau translocation to excitatory synapses may be involved in excitotoxicity in PD pathology [41]. These findings suggest that tau, as an underappreciated component, plays a role in PD pathology and may provide a new therapeutic target for PD.

[123I] FP-CIT SPECT, a DAT imaging technique, allows in vivo assessment of striatal DA reuptake transporter concentrations. Alex et al. found that idiopathic RBD participants exhibited reduced mean striatal [123I] FP-CIT uptake, with a more pronounced effect in the putamen than the caudate [42]. Similar findings have been reported in PD patients, where the mean [123I] FP-CIT uptake was lower and decreased faster in the putamen compared to the caudate [43]. These observations are consistent with post-mortem studies in PD which demonstrate earlier and more severe DA depletion in the putamen than in the caudate [19, 44]. In addition, our study found that the putamen was a better predictor of sleep disturbance scores (including ESS and RBDSQ) compared to the caudate.

Our study has several limitations that should be acknowledged. First, the PPMI database utilized in this study primarily consists of patients with newly diagnosed PD. Additionally, the follow-up period for SPECT imaging was only 4 years. These factors may limit the generalizability of our findings to patients with more advanced stages of PD. Second, the diagnoses of both RBD and EDS were based on questionnaires rather than confirmatory polysomnography. While a large sample size can mitigate some uncertainty in diagnosis using questionnaires, polysomnography remains the gold standard for definitive diagnosis. Third, due to incomplete information regarding medication history for RBD treatment, including details such as medication end times and dosages, we could not include this data as an additional covariate for adjustment. Fourth, although healthy controls underwent baseline SPECT imaging, they lacked subsequent follow-up imaging, precluding longitudinal comparisons between the PD and control groups. Finally, the SNCA-PD group included only 15 individuals. This relatively small sample size may limit the statistical power and generalizability of the findings for this specific group.

Conclusions

In conclusion, our findings suggest a strong association between striatal dopaminergic dysfunction, as measured by striatal DAT SBR using [123I] FP-CIT SPECT, and sleep disturbances (EDS and pRBD) in PD. This association appears progressively stronger over time in patients with pRBD. Furthermore, we observed that reduced striatal DAT SBR is a risk factor for developing sleep disturbances in PD patients. Additionally, our study revealed a significant mediation effect of striatal DAT SBR on the relationship between specific SNCA mutations and sleep disturbances. These findings may offer valuable insights into the potential utility of striatal DAT SBR imaging as a tool for the early identification of PD patients at risk for sleep disturbances. Moreover, they may contribute to understanding how specific SNCA mutations might influence disease progression through their impact on striatal dopamine function.

Data availability

PPMI is an open access dataset. Data used in the preparation of this manuscript were obtained from the PPMI database (www.ppmi-info.org/data). Study protocol and manuals are available at www.ppmi-info.org/study-design.

Abbreviations

PD:

Parkinson’s disease

EDS:

Excessive daytime sleepiness

RBD:

Rapid eye movement sleep behavior disorder

SPECT:

Single-photon emission computed tomography

DA:

Dopamine

DAT:

DA transporter

SBR:

Specific binding ratios

PPMI:

Parkinson’s Progression Markers Initiative

ESS:

Epworth Sleepiness Scale

RBDSQ:

RBD Screening Questionnaire

pRBD:

Probable RBD

VTA:

Ventral tegmental area

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Acknowledgements

PPMI - a public-private partnership - is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's, AskBio, Avid Radiopharmaceuticals, BIAL, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.

Funding

This study was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LQ24H090003), the Fundamental Research Funds for the Central Universities (No. 2023FZZX05-02), and the National Natural Science Foundation of China (No. 82302081).

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Author notes

  1. Junli Ren, Haobo Xie and Yiyun Weng are co-first authors.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China

    Junli Ren

  2. Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

    Haobo Xie, Yiyun Weng, Yaoying Ge, Ruotong Yao, Zihan Jiang, Jinxiu Zhang, Yusheng Zhu, Xiaotong Fu, Junchao Wang, Zijia Liu, Shishu Zhang & Tingxuan Zhang

  3. The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Haobo Xie, Yaoying Ge, Ruotong Yao & Xiaotong Fu

  4. The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Zihan Jiang, Jinxiu Zhang, Yusheng Zhu, Junchao Wang & Zijia Liu

  5. Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, China

    Shishu Zhang & Tingxuan Zhang

  6. Department of Neurology, The Third Affiliated Hospital of Wenzhou Medical University, No.108 Wansong Road, Wenzhou, Zhejiang, 325000, China

    Guangyong Chen

  7. Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou , Zhejiang, 310009, China

    Dehao Yang

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Contributions

GC and DY conceptualized this work. GC and DY supervised the study. JR, HX, YW, YG, RY, ZJ, JZ, YZ, XF, JW, ZL, SZ, and TZ contributed in the acquisition of data. JR, HX, and YW performed the statistical analysis and interpreted data. JR, HX, and YW prepared the manuscript. GC, DY, JR, HX, YW, YG, RY, ZJ, JZ, YZ, XF, JW, ZL, SZ, and TZ revised the manuscript. All authors approved the protocol. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Guangyong Chen or Dehao Yang.

Ethics declarations

Ethics approval and consent to participate

Data used in the preparation of this article were obtained on [2023-08-19] from the PPMI database (https://www.ppmi-info.org/access-data-specimens/download-data), RRID: SCR_006431. For up-to-date information on the study, visit http://www.ppmi-info.org. All participating institutions’ regional ethical committees approved the PPMI, and written informed consent were obtained from all study participants.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

12916_2024_3766_MOESM1_ESM.pdf

Additional file 1: Fig. S1. Statistical analysis results of the striatal dopamine transporter (DAT) specific binding ratio (SBR). Linear mixed effects model investigating the relationship between DAT SBR and sleep disturbances score. A for caudate and Epworth Sleepiness Scale (ESS) score; B for putamen and ESS score; C for caudate and Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score. D for putamen and RBDSQ score. Multivariate logistic regression analysis investigating the relationship between DAT SBR and sleep disturbances. E for caudate and excessive daytime sleepiness (EDS); F for putamen and EDS; G for caudate and probable rapid eye movement sleep behavior disorder (pRBD). H for putamen and pRBD. Points represent estimated regression coefficients and bars represent 95% CIs; *p < 0.05; **p < 0.01; ***p < 0.001. Fig. S2. Linear mixed effects model investigating the longitudinal relationship between striatal dopamine transporter (DAT) binding ratio (SBR) and sleep disturbances score at post-baseline assessments, adjusted for baseline striatal DAT SBR (this makes the striatal DAT SBR main effect equivalent to change from baseline). Points represent estimated regression coefficients and bars represent 95% confidence intervals; p < 0.05*, p < 0.01**, p < 0.001***. Fig. S3. Association between striatal dopamine transporter (DAT) specific binding ratio (SBR) and sleep disturbances score using change-from-baseline (BL) SBR values (top) and absolute SBR values (bottom). A for Epworth Sleepiness Scale (ESS) score; B for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score. Individual linear regression models at different years are displayed. Points represent estimated regression coefficients and bars represent 95% confidence intervals; p < 0.05*, p < 0.01**, p < 0.001***. Fig. S4. Mediation analysis of GBA mutation for sleep disturbances scores through striatal dopamine transporter (DAT) specific binding ratio (SBR). A for Epworth Sleepiness Scale (ESS) score; B for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score. Fig. S5. Mediation analysis of LRRK2 mutation for sleep disturbances scores through striatal dopamine transporter (DAT) specific binding ratio (SBR). A for Epworth Sleepiness Scale (ESS) score; B for Rapid Eye movement Sleep Behavior Disorders Screening Questionnaire (RBDSQ) score.

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Ren, J., Xie, H., Weng, Y. et al. Longitudinal decline in DAT binding in Parkinson’s disease: connections with sleep disturbances. BMC Med 22, 550 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12916-024-03766-5

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BMC Medicine

ISSN: 1741-7015

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