Abstract
Background and purpose
Glymphatic dysfunction may play a significant role in the development of neurodegenerative diseases. We aimed to evaluate the association between glymphatic dysfunction and the risk of malignant event/clinical milestones in Parkinson disease (PD).
Methods
This study included 236 patients from August 2014 to December 2020. Diffusion tensor imaging analysis along the perivascular space (DTI‐ALPS) index was calculated as an approximate measure of glymphatic function. The primary outcomes were four clinical milestones including recurrent falls, wheelchair dependence, dementia, and placement in residential or nursing home care. The associations of DTI‐ALPS with the risk of clinical milestones were examined using multivariate Cox proportional hazards regression models. Then, logistic regression was repeated using clinical variables and DTI‐ALPS index individually and in combination of the two to explore the ability to distinguish patients who reached clinical milestones within a 5‐year period.
Results
A total of 175 PD patients with baseline DTI‐ALPS index and follow‐up clinical assessments were included. A lower DTI‐ALPS was independently associated with increased risk of recurrent falls, wheelchair dependence, and dementia. Additionally, in 103 patients monitored over 5 years, a logistic regression model combining clinical variables and DTI‐ALPS index showed better performance for predicting wheelchair dependence within 5 years than a model using clinical variables or DTI‐ALPS index alone.
Conclusions
Glymphatic dysfunction, as measured by the DTI‐ALPS index, was associated with increased risk of clinical milestones in patients with PD. This finding implies that therapy targeting the glymphatic system may serve as a viable strategy for slowing down the progression of PD.
Keywords: dementia, falls, glymphatic system, magnetic resonance imaging, Parkinson disease
INTRODUCTION
Parkinson disease (PD) affects nearly 6.1 million individuals in the world [1, 2]. Over the time span between 1990 and 2016, age‐standardized prevalence and disability‐adjusted life years because of PD have doubled [1]. Prior to death, patients often experience four malignant events/clinical milestones: recurrent falls, wheelchair dependence, dementia, and nursing home care placement. These events provide valuable information on PD prognosis [3, 4]. Although dopaminergic medications improve some aspects of these milestones, some features appear refractory to current interventions and incur high associated cost of care [5]. Therefore, a better understanding of the associated risk factors is warranted to help tailor treatment approaches to improve PD outcomes.
In recent years, researchers have focused their attention on the role of the glymphatic system, an astrocytic aquaporin 4 (AQP4)‐dependent drainage system responsible for clearing pathological substances within the brain [6]. Several studies have confirmed the clearance function of the glymphatic system in animal models and further identified its contribution to neurological disorders [7, 8, 9]. A previous study has preliminarily confirmed the relationship between glymphatic dysfunction and PD from both human and animal model experiments, suggesting that glymphatic dysfunction aggravates α‐synuclein pathology in a PD mice model [10]. Therefore, a well‐functioning glymphatic system, which is involved in waste clearance, appears to be crucial for delaying or preventing neurodegenerative processes and clinical disabilities in patients with PD [7, 11].
Currently, glymphatic system function can be assessed by a variety of means. Glymphatic magnetic resonance imaging (MRI) after intrathecal administration of gadolinium is the gold standard for evaluating glymphatic function [12, 13]. However, widespread use of gadolinium‐based contrast agents is unrealistic, and at doses > 1.0 mmol/kg, severe neurotoxic complications may occur [14]. In addition to invasive assessment methods, recent noninvasive MRI measures, such as diffusion tensor imaging analysis along the perivascular space (DTI‐ALPS) index, have seen expanded application for the assessment of glymphatic system function [15]. The stability and reliability of the DTI‐ALPS index have been reasonably validated [16]. Several studies have reported a significant decrease in the DTI‐ALPS index among patients with PD and investigated its association with motor and cognitive symptoms [17, 18, 19, 20, 21]. Preliminary longitudinal studies have investigated the association between DTI‐ALPS index and the deterioration in motor and cognitive performance [22, 23]. However, the relationship between glymphatic dysfunction and milestones such as falls, wheelchair use, dementia, and home care remain unexplored [22, 23]. Considering that glymphatic function is a clinically modifiable aspect, investigating the relationship between these factors may offer novel targets and strategies for future interventions and treatments, with the potential to enhance the quality of life and disease management for individuals with PD.
In this retrospective study, we employed DTI‐ALPS index to quantify glymphatic function of patients with PD. Cox proportional hazard analyses were performed to assess the relation between the noninvasive glymphatic measure and four clinical milestones in PD patients. Moreover, logistic regression models were used to investigate the relationship of the baseline DTI‐ALPS index with the odds of reaching clinical milestones within 5 years.
METHODS
Participants
In this retrospective study, a total of 236 patients with diagnosed PD who visited the movement disorders outpatient clinic at the Second Affiliated Hospital of Zhejiang University between August 2014 and December 2020 were enrolled in the PD cohort. All patients fulfilled Movement Disorder Society Clinical Diagnostic Criteria for PD at their last interview [24]. Patients with a history of brain tumors, stroke, cerebral trauma, psychiatric disorders, non‐central nervous system malignancies, substance abuse, or participation in other trials were not included. Due to insufficient awareness of transient ischemic attacks (TIAs) among patients and doctors, leading to an underestimation of their incidence, TIA is currently not included as an exclusion criterion in this study. Additionally, patients who were already experiencing recurrent falls, wheelchair dependency, dementia, or being bedridden at the time of enrollment were not included.
The protocol, consent form, and other relevant documentation were approved by the ethics committee of the local hospital before the study commenced. The study was performed in accordance with the Declaration of Helsinki. Before enrollment, all patients provided their written informed consent.
Clinical evaluation
At baseline, motor function was assessed with part III of the Unified Parkinson's Disease Rating Scale (UPDRS‐III). Global cognition was assessed with the Mini‐Mental State Examination (MMSE). Clinical assessments were conducted during off state (at least 12 h after withholding PD medications). Additionally, data on body mass index (BMI), smoking status, alcohol drinking status, and histories of hypertension, diabetes mellitus, and coronary heart disease were collected at baseline. BMI was calculated as weight in kilograms divided by height in meters squared. A smoker was defined as someone smoking at least 1 cigarette per week. Alcohol drinkers were defined as those drinking at least 1 drink per week. Hypertension was defined by blood pressure readings consistently exceeding 140/90 mmHg [25]. Diabetes mellitus and coronary heart disease histories were taken from electronic health records.
Survival
Participants in this study visited the movement disorders outpatient clinic every 6–12 months for routine care and regularly shared updates on their disease progression with clinical milestones via WeChat or phone calls. To evaluate disease progression, we recorded the time from inclusion to specific disease milestones, including (i) recurrent falls, defined as experiencing more than two falls per year [26, 27]; (ii) wheelchair dependence, indicating motor disability; (iii) dementia, diagnosed by a movement disorder expert in clinic according to the Level I criteria proposed by the Movement Disorder Society Task Force: (a) diagnosis of PD, (b) PD developed 1 year or more prior to the onset of dementia, (c) MMSE < 24 [28], (d) cognitive deficits severe enough to impact daily tasks, and (e) impairment in at least two cognitive domains [29]; and (iv) placement in residential or nursing home care, reflecting overall disability. We also documented the time from diagnosis to death.
Image acquisition
The acquisition of imaging data was conducted within 1 week following the baseline clinical assessment. All participants were scanned on a GE Discovery MR750 3.0‐T MRI scanner. Earplugs and foam pads were used to reduce noise and head motion. DTI data were acquired using a spin echo–echo planar imaging sequence (repetition time = 8000 ms, echo time = 80 ms, flip angle = 90°, field of view = 256 × 256 mm2, matrix = 128 × 128, slice thickness = 2 mm, slice gap = 0 mm, number of slices = 67 [axial]). DTI was acquired from 30 gradient directions (b = 1000 s/mm2) and included five acquisitions without diffusion weighting (b = 0).
Diffusion tensor imaging analysis along the perivascular space
DTI data were processed using the FMRIB Software Library (FSL; http://www.fmrib.ox.ac.uk/fsl) and MRtrix3 (http://www.mrtrix.org). First, DTI data were preprocessed via denoising, removing Gibbs ringing artifact. Next, “eddy_correct” was conducted to correct eddy currents and movements in DTI data. Skull stripping was then performed from the DTI data for each participant. Following this, the fractional anisotropy (FA) map and three distinct diffusivity maps—Dxx (x‐axis), Dyy (y‐axis), and Dzz (z‐axis)—were computed using “dtifit.”
The DTI‐ALPS index is an approximate measure of glymphatic function in vivo, and a higher DTI‐ALPS index suggests better glymphatic function [15]. DTI‐ALPS index evaluates diffusivity along the perivascular space (PVS) on axial slices at the level of the lateral ventricle bodies. At this level, the medullary veins run alongside the PVS, perpendicular to the ventricular walls (x‐axis). The corticofugal corona radiata projection fibers run in the craniocaudal direction (z‐axis), adjacent to the lateral ventricle. The superior longitudinal fascicle, representing association fibers, runs in the anterior–posterior direction (y‐axis) and is located lateral to the corona radiata [15]. The direction of water diffusion along the PVS (x‐axis) is perpendicular to both the superior longitudinal fascicle association fiber regions (y‐axis) and corticofugal corona radiata projection fibers (z‐axis). Therefore, the diffusivity measured along the x‐axis in the projection fibers and association fiber regions (Dxproj and Dxassoc) can indicate the functioning of the glymphatic system through perivascular water movement, with minimal influence from the diffusivities of the neural fibers. In addition, Dxproj and Dxassoc values were divided by the diffusivities perpendicular to them—Dyproj and Dzassoc—to minimize the effect from the individual white matter degeneration. Therefore, the DTI‐ALPS index is defined as follows:
To mitigate potential bias stemming from manually drawn regions of interest (ROIs), we employed an atlas‐based approach according to a previous study [30]. In summary, FA maps in native space were linearly and nonlinearly registered to the FA template in Montreal Neurological Institute space using FSL. Subsequently, three distinct diffusivity maps of the subjects were warped using the transformation matrix from the FA maps. ROIs encompassing the bilateral projection (superior and posterior corona radiata) and association (superior longitudinal fasciculus) fibers were extracted based on the atlas labels. A mask was applied, ensuring an FA threshold of >0.2 to exclude cerebrospinal fluid (CSF) voxels. The ROIs were limited in the craniocaudal direction to areas where the x‐axis lines were perpendicular to the lateral ventricle bodies. Finally, to remove the possible impact of white matter integrity, we further computed the average FA and mean diffusivity for the entire brain's white matter of each subject, following the methodology established in a previous study [31].
Statistics
Descriptive statistics including mean, SD, frequencies, and percentages were used to describe demographic and clinical characteristics. Univariate and stepwise multivariate Cox proportional hazards regression models were used to estimate the risk of four clinical milestones for the baseline DTI‐ALPS index. Other potential confounders, including baseline age, sex, educational level, PD duration, UPDRS‐III and MMSE scores, BMI, smoking and alcohol drinking status, and histories of hypertension, diabetes mellitus, and coronary heart disease were included in the models as explanatory variables. Only those with statistical significance (p < 0.05) in the univariate analysis were included in the final multivariate model. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated. The proportional hazards assumption was tested using the correlation between Schoenfeld residuals and survival time. Logistic regression was repeated using clinical variables (age, sex, educational level, BMI, smoking and alcohol drinking status, histories of hypertension, diabetes mellitus, and coronary heart disease, PD duration, UPDRS‐III and MMSE scores) and DTI‐ALPS index individually and in combination with the clinical variables and DTI‐ALPS index to explore the ability to distinguish patients who reached clinical milestones within a 5‐year period. The variance inflation factor for each covariate was calculated to avoid multicollinearity, and any collinear covariates were removed if the variance inflation factor was >5. The area under the receiver operator characteristic curve (AUC) for the clinical variables or DTI‐ALPS index alone was statistically compared against the combination of clinical and DTI‐ALPS index using DeLong's test. All statistical analyses were performed with R (v.3.6.1) and IBM SPSS Statistics 26. All statistical tests were two‐sided, and statistical significance was determined at p < 0.05.
RESULTS
In this cohort, 12 patients lacked available MRI data, 48 patients withdrew or were lost to follow‐up, and the diagnosis for one patient was changed to multiple system atrophy. Therefore, 175 patients were finally enrolled in this study, and 103 were monitored for up to 5 years. The patient selection process is presented in Figure 1. The demographic, clinical, and imaging features at baseline are shown in Table 1. No patients met eligibility for clinical milestones at the time of baseline imaging and clinical assessment. Patients were followed for an average of 5.44 years, with an SD of 1.63 years. Cumulative risk of disease milestones is shown in Figure 2. Of the 175 patients studied, 55 developed recurrent falls, 24 developed wheelchair dependence, and 15 developed dementia. Twelve of the 175 patients were placed in residential or nursing home care during follow‐up, and 13 patients died during follow‐up. The most common causes of death in this PD cohort are pneumonia and accidents, consistent with a previous study [32]. The causes of death varied, and many could not be directly attributed to PD; therefore, the study did not analyze the mortality risk ratio in relation to the DTI‐ALPS index.
FIGURE 1.
Flowchart of patients included in the study. MRI, magnetic resonance imaging; MSA, multiple system atrophy.
TABLE 1.
Characteristics of the study participants.
| Characteristic | Parkinson disease, n = 175 |
|---|---|
| Age, years, mean (SD) | 59.67 (9.62) |
| Male sex, n (%) | 102 (58) |
| Education, years, mean (SD) | 8.03 (4.65) |
| Disease duration, years, mean (SD) | 3.72 (3.33) |
| UPDRS‐III, mean (SD) | 22.12 (12.98) |
| MMSE, mean (SD) | 26.75 (3.68) |
| Body mass index, mean (SD) | 23.26 (2.62) |
| Smoking, n (%) | 44 (25) |
| Alcohol drinking, n (%) | 50 (29) |
| Hypertension, n (%) | 56 (32) |
| Diabetes mellitus, n (%) | 21 (12) |
| Coronary heart disease, n (%) | 5 (3) |
| DTI‐ALPS index, mean (SD) | 1.72 (0.19) |
| Follow‐up time, years, mean (SD) | 5.44 (1.63) |
Abbreviations: DTI‐ALPS, diffusion tensor imaging analysis along the perivascular space; MMSE, Mini‐Mental State Examination; UPDRS, part III of the Unified Parkinson's Disease Rating Scale.
FIGURE 2.
Cumulative risk of disease milestones, including recurrent falls, wheelchair dependence, dementia, and care home placement in this cohort. Kaplan–Meier survival curves with at‐risk table illustrate the cumulative probability of remaining free of four clinical milestones including recurrent falls, wheelchair dependence, dementia, and placement in residential or nursing home care (shortened as “home care” in the figure) in a Parkinson disease cohort followed for up to 8 years.
We explored whether baseline DTI‐ALPS index could predict progression to the clinical milestones using Cox proportional hazards regression analysis. In the univariate model, lower DTI‐ALPS index (HR = 0.09, 95% CI = 0.02–0.38, p = 0.001) predicted more rapid progression to recurrent falls. As reported in Figure 3a, in the multivariable regression analysis that was adjusted for sex, age, level of education, disease duration, UPDRS‐III, MMSE, and hypertension, DTI‐ALPS index remained independent factors associated with the development of recurrent falls (HR = 0.08, 95% CI = 0.02–0.36, p = 0.001). Lower DTI‐ALPS index was predictive of more rapid progression to wheelchair dependence (univariate HR = 0.03, 95% CI = 0.00–0.25, p = 0.001). This remained significant following multivariate analysis (adjusted HR = 0.04, 95% CI = 0.01–0.34, p = 0.003; Figure 3b). Additionally, lower DTI‐ALPS index was independently associated with increased dementia risk (univariate HR = 0.05, 95% CI = 0.00–0.72, p = 0.027; adjusted HR = 0.04, 95% CI = 0.00–0.63, p = 0.023; Figure 3c). However, the DTI‐ALPS index was not significantly associated with the risk of placement in residential or nursing home care (univariate HR = 0.21, 95% CI = 0.01–4.16, p = 0.306).
FIGURE 3.
Association between baseline diffusion tensor imaging analysis along the perivascular space (DTI‐ALPS) index and the risk of clinical milestones among individuals with Parkinson disease. Sex, age, educational level, disease duration, part III of the Unified Parkinson's Disease Rating Scale (UPDRS‐III) score, Mini‐Mental State Examination (MMSE) score, body mass index, smoking and alcohol drinking status, and histories of hypertension, diabetes mellitus, and coronary heart disease were included in the Cox proportional hazard regression models as explanatory variables, but only those that retained statistical significance after adjustment in the multivariate analysis were included in the final model. Hazard ratio (HR) > 1 indicates that for an increase in the corresponding variable, there is a higher risk of clinical milestone. HR < 1 indicates that increase in the corresponding variable is associated with a low risk of developing the clinical milestone. Error bars represent the confidence interval (CI).
In addition to DTI‐ALPS index, demographic and clinical data were useful in predicting the occurrence of clinical milestones. In the multivariate regression analysis, older age and higher UPDRS‐III score were independently associated with increased risk of recurrent falls (HR = 1.04, 95% CI = 1.00–1.08, p = 0.029 and HR = 1.04; 95% CI = 1.02–1.06, p = 0.001, respectively) and wheelchair dependence (HR = 1.06, 95% CI = 1.01–1.11, p = 0.023 and HR = 1.03, 95% CI = 1.00–1.06, p = 0.029, respectively); older age (HR = 1.15, 95% CI = 1.07–1.24, p < 0.001) was independently associated with an increased risk of dementia (Figure 3). Older age (HR = 1.09, 95% CI = 1.01–1.17, p = 0.022) and higher UPDRS‐III score (HR = 1.04, 95% CI = 1.00–1.09, p = 0.030) were independently associated with increased risk of placement in residential or nursing home care. Detailed results of univariate Cox regression model analysis for risk of clinical milestones are described in Table S1.
We further explored whether DTI‐ALPS index could help predict the development of clinical milestones within a 5‐year period when combined with clinical variables. Of the 175 patients, 103 were monitored for up to 5 years. Details about this subgroup are presented in Table S2. Baseline clinical variables including age, sex, and UPDRS‐III score were able to distinguish patients who developed recurrent falls within 5 years with an AUC of 0.80 (95% CI = 0.71–0.88, sensitivity = 0.85, specificity = 0.69, accuracy = 0.75). Baseline DTI‐ALPS index was also able to distinguish patients who developed recurrent falls with an AUC of 0.71 (95% CI = 0.60–0.81, sensitivity = 0.79, specificity = 0.58, accuracy = 0.66). An AUC of 0.85 (95% CI = 0.78–0.92, sensitivity = 0.72, specificity = 0.83, accuracy = 0.79) was noted in the model combining clinical variables and DTI‐ALPS index (Figure 4a). The AUC for the model that combined clinical variables and DTI‐ALPS index had a notably higher AUC than the model with DTI‐ALPS index only, but not the model with clinical variables only (0.85 vs. 0.71 and 0.85 vs. 0.80, DeLong's test p = 0.012 and 0.155, respectively). In distinguishing patients who did and did not develop wheelchair dependence, the AUC for clinical variable (age only) alone was 0.70 (95% CI = 0.59–0.81, sensitivity = 0.50, specificity = 0.88, accuracy = 0.81) and for DTI‐ALPS only was 0.71 (95% CI = 0.60–0.81, sensitivity = 0.55, specificity = 0.78, accuracy = 0.73), compared to 0.83 (95% CI = 0.75–0.91, sensitivity = 0.80, specificity = 0.77, accuracy = 0.78) when combining clinical variables with DTI‐ALPS index (Figure 4b). The addition of DTI‐ALPS index to the clinical variables significantly enhanced the performance of models with DTI‐ALPS index or clinical variables only (DeLong's test p = 0.048 and 0.012, respectively). Moreover, baseline clinical variables including age and disease duration were able to distinguish patients who developed dementia within 5 years with an AUC of 0.87 (95% CI = 0.75–0.99, sensitivity = 0.85, specificity = 0.84, accuracy = 0.84). Baseline DTI‐ALPS index was also able to distinguish patients who developed dementia with an AUC of 0.72 (95% CI = 0.58–0.85, sensitivity = 0.77, specificity = 0.62, accuracy = 0.64). An AUC of 0.90 (95% CI = 0.79–1.00, sensitivity = 0.85, specificity = 0.89, accuracy = 0.88) was noted in the model combining clinical variables and DTI‐ALPS index (Figure 4c). The AUC for the model that combined clinical variables and DTI‐ALPS index was not significantly higher than the models with DTI‐ALPS index or clinical variables only (0.90 vs. 0.72 and 0.90 vs. 0.87, DeLong's test p = 0.072 and 0.158, respectively). Lastly, baseline clinical variables including UPDRS‐III and BMI were able to distinguish patients who were placed in residential or nursing home care with an AUC of 0.83 (95% CI = 0.71–0.95, sensitivity = 0.99, specificity = 0.63, accuracy = 0.66). However, the DTI‐ALPS index could not distinguish patients who were placed in residential or nursing home care within 5 years.
FIGURE 4.
Performance of the predictive model for predicting the development of clinical milestones. Predictive models are shown based on the clinical variables or diffusion tensor imaging analysis along the perivascular space (DTI‐ALPS) index individually and on the combination of clinical and magnetic resonance imaging variables for predicting the development of recurrent falls (a), wheelchair use (b), and dementia (c) within 5 years. AUC, area under the receiver operator characteristic curve.
DISCUSSION
In this longitudinal and retrospective study, we investigated the association between glymphatic system dysfunction, measured by the DTI‐ALPS index, and the risk of clinical milestones in patients with PD. Our study shows that lower DTI‐ALPS index was independently associated with increased risk of recurrent falls, wheelchair dependence, and dementia. DTI‐ALPS index could help predict the development of recurrent falls, wheelchair dependence, and dementia within a 5‐year period. Additionally, the combination of the DTI‐ALPS index with clinical variables notably improves the accuracy of predicting wheelchair dependence over a 5‐year span, in contrast to using either clinical variables or the DTI‐ALPS index alone.
A previous study found that injecting mice with preformed α‐synuclein fibrils led to the development of α‐synuclein pathology in brain, causing their meningeal glymphatic drainage system to work less effectively [10]. In turn, the reduced efficiency of the glymphatic drainage system then worsened the α‐synuclein pathology aggravation [10, 33]. They also found that PD patients showed significantly decreased meningeal lymphatic flow compared to healthy controls using dynamic contrast‐enhanced MRI. As a noninvasive and reliable surrogate measure for dynamic contrast‐enhanced MRI [16], DTI‐ALPS index has been extensively applied across various neurological conditions, including Alzheimer disease, PD, multiple sclerosis, and amyotrophic lateral sclerosis [18, 34, 35, 36]. Previous studies have consistently shown a reduced DTI‐ALPS index in patients with PD [17, 18, 37, 38]. A decrease in the DTI‐ALPS index has been linked with motor symptoms such as freezing of gait and cognitive deficits in PD patients [19, 20, 21, 37]. Moreover, recent multicenter studies have suggested that a lower DTI‐ALPS index correlates with accelerated motor and cognitive decline over 5 years [22, 23]. These studies could support our findings that the DTI‐ALPS index was associated with the incidents of falls, wheelchair dependence, and dementia in PD. Considering that glymphatic clearance is modifiable, future therapeutic approaches targeting the glymphatic system—such as modulation of sleep architecture, AQP4, or CSF flow in PVS—may improve outcomes for PD patients [11, 39, 40].
We found that the DTI‐ALPS index offers predictive value for the onset of clinical milestones within 5 years, including recurrent falls, wheelchair dependence, and dementia. These findings were consistent with the results of Cox proportional hazards regression models. A recent small‐sample longitudinal study indicated that DTI‐ALPS index at baseline was associated with longitudinal cognitive decline and Movement Disorders Society UPDRS total score increase in PD [41]. However, the DTI‐ALPS index does not show a significant association with the risk of home care placement over a 5‐year period, which could be attributed to the relatively small number of patients developing overall disability within that timeframe. Moreover, we found that the model combining the clinical features with DTI‐ALPS index could significantly improve the prediction of wheelchair dependence over using clinical features or DTI‐ALPS index respectively. This finding suggests that the DTI‐ALPS index may help stratify patients with PD based on their risk of developing a malignant prognosis.
LIMITATIONS
This study has several limitations. The limited number of participants and the possibility of uncontrolled confounding factors are limitations of this observational study. Although our study has a limited sample size, considering that data from large, long‐term follow‐up studies are extremely difficult to obtain, our research still holds significance. Additionally, we recognize that noninvasive MRI methods do not directly measure glymphatic function, although the DTI‐ALPS index was highly correlated with glymphatic function classically detected through glymphatic MRI after intrathecal administration of gadolinium [16]. Although this noninvasive methodology cannot be regarded as a direct substitute for the gold standard in assessing glymphatic function, it nonetheless offers valuable insights into the potential determinants of adverse prognostic events in PD. Validating the present findings through invasive gold standard approaches would be beneficial. We also acknowledge that the retrospective design may introduce certain biases. Potential prognostic factors, such as serum lipid and comprehensive cognitive assessments, were not considered at baseline; therefore, the current findings should be interpreted with caution.
CONCLUSIONS
Glymphatic dysfunction, as indicated by the DTI‐ALPS index, is an independent risk factor for recurrent falls, wheelchair dependence, and dementia. This measure may provide some value in identifying PD patients at risk for adverse outcomes within 5 years. These findings also suggest that future therapies targeting glymphatic dysfunction could represent a viable strategy for slowing the progression of PD.
AUTHOR CONTRIBUTIONS
Minming Zhang had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Cheng Zhou, Xianchen Jiang, and Minming Zhang. Acquisition, analysis, or interpretation of data: Cheng Zhou, Xianchen Jiang, Xiaojun Guan, Tao Guo, Jingjing Wu, Haoting Wu, Chenqing Wu, Jingwen Chen, Jiaqi Wen, Sijia Tan, Xioajie Duanmu, Jianmei Qin, Weijin Yuan, Qianshi Zheng, and Baorong Zhang. Drafting of the manuscript: Cheng Zhou. Critical review of the manuscript for important intellectual content: All authors. Statistical analysis: Cheng Zhou and Xianchen Jiang. Obtained funding: Minming Zhang, Cheng Zhou, Xiaojun Guan, Tao Guo, Jingjing Wu, Peiyu Huang, and Xiaojun Xu. Administrative, technical, or material support: Minming Zhang. Supervision: Minming Zhang.
FUNDING INFORMATION
This work was supported by the National Natural Science Foundation of China (82271935, 82302132, 82171888, 82202091, 82001767, 91630314, 82071997, 82302136, 82202089, 82371906, and 81971577), the Natural Science Foundation of Zhejiang Province (LY22H180002, LQ21H180008, and Z24H180002), the 13th Five‐Year Plan for National Key Research and Development Program of China (2016YFC1306600), and the China Postdoctoral Science Foundation (2023M733085).
CONFLICT OF INTEREST STATEMENT
None of the authors has any financial interests or relationships to disclose that could inappropriately influence, or be perceived to influence, the work presented in this article.
ETHICS STATEMENT
The study was conducted in accordance with the Declaration of Helsinki. The protocol, consent form, and other relevant documentation were approved by the local ethics committee before the study commenced.
PATIENT CONSENT STATEMENT
All participants provided written informed consent before enrollment.
Supporting information
Table S1.
ACKNOWLEDGMENTS
We thank Shanghai Tengyun Biotechnology Co. for developing the Hiplot Pro platform (https://hiplot.com.cn/) and providing valuable tools for data analysis and visualization. Hiplot is a free tool.
Zhou C, Jiang X, Guan X, et al. Glymphatic system dysfunction and risk of clinical milestones in patients with Parkinson disease. Eur J Neurol. 2024;31:e16521. doi: 10.1111/ene.16521
Cheng Zhou and Xianchen Jiang contributed equally to the work.
Xiaojun Xu and Minming Zhang contributed equally to the work as co‐senior authors.
Contributor Information
Xiaojun Xu, Email: xxjmailbox@zju.edu.cn.
Minming Zhang, Email: zhangminming@zju.edu.cn.
DATA AVAILABILITY STATEMENT
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
REFERENCES
- 1. GBD 2016 Neurology Collaborators . Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):459‐480. doi: 10.1016/s1474-4422(18)30499-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Kalia LV, Lang AE. Parkinson's disease. Lancet. 2015;386(9996):896‐912. doi: 10.1016/s0140-6736(14)61393-3 [DOI] [PubMed] [Google Scholar]
- 3. Kempster PA, O'Sullivan SS, Holton JL, Revesz T, Lees AJ. Relationships between age and late progression of Parkinson's disease: a clinico‐pathological study. Brain. 2010;133(Pt 6):1755‐1762. doi: 10.1093/brain/awq059 [DOI] [PubMed] [Google Scholar]
- 4. Gonzalez MC, Dalen I, Maple‐Grødem J, Tysnes OB, Alves G. Parkinson's disease clinical milestones and mortality. NPJ Parkinsons Dis. 2022;8(1):58. doi: 10.1038/s41531-022-00320-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Venuto CS, Smith G, Herbst K, et al. Predicting ambulatory capacity in Parkinson's disease to analyze progression, biomarkers, and trial design. Mov Disord. 2023;38(10):1774‐1785. doi: 10.1002/mds.29519 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Sun BL, Wang LH, Yang T, et al. Lymphatic drainage system of the brain: a novel target for intervention of neurological diseases. Prog Neurobiol. 2018;163–164:118‐143. doi: 10.1016/j.pneurobio.2017.08.007 [DOI] [PubMed] [Google Scholar]
- 7. Lopes DM, Llewellyn SK, Harrison IF. Propagation of tau and α‐synuclein in the brain: therapeutic potential of the glymphatic system. Transl Neurodegener. 2022;11(1):19. doi: 10.1186/s40035-022-00293-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Iliff JJ, Wang M, Liao Y, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med. 2012;4(147):147ra111. doi: 10.1126/scitranslmed.3003748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nedergaard M, Goldman SA. Glymphatic failure as a final common pathway to dementia. Science (New York, NY). 2020;370(6512):50‐56. doi: 10.1126/science.abb8739 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Ding XB, Wang XX, Xia DH, et al. Impaired meningeal lymphatic drainage in patients with idiopathic Parkinson's disease. Nat Med. 2021;27(3):411‐418. doi: 10.1038/s41591-020-01198-1 [DOI] [PubMed] [Google Scholar]
- 11. Gao Y, Liu K, Zhu J. Glymphatic system: an emerging therapeutic approach for neurological disorders. Front Mol Neurosci. 2023;16:1138769. doi: 10.3389/fnmol.2023.1138769 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Ringstad G, Vatnehol SAS, Eide PK. Glymphatic MRI in idiopathic normal pressure hydrocephalus. Brain. 2017;140(10):2691‐2705. doi: 10.1093/brain/awx191 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Eide PK, Ringstad G. MRI with intrathecal MRI gadolinium contrast medium administration: a possible method to assess glymphatic function in human brain. Acta Radiol Open. 2015;4(11):2058460115609635. doi: 10.1177/2058460115609635 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Patel M, Atyani A, Salameh JP, McInnes M, Chakraborty S. Safety of Intrathecal Administration of Gadolinium‐based Contrast Agents: a systematic review and meta‐analysis. Radiology. 2020;297(1):75‐83. doi: 10.1148/radiol.2020191373 [DOI] [PubMed] [Google Scholar]
- 15. Taoka T, Masutani Y, Kawai H, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI‐ALPS) in Alzheimer's disease cases. Jpn J Radiol. 2017;35(4):172‐178. doi: 10.1007/s11604-017-0617-z [DOI] [PubMed] [Google Scholar]
- 16. Zhang W, Zhou Y, Wang J, et al. Glymphatic clearance function in patients with cerebral small vessel disease. NeuroImage. 2021;238:118257. doi: 10.1016/j.neuroimage.2021.118257 [DOI] [PubMed] [Google Scholar]
- 17. Bae YJ, Kim JM, Choi BS, et al. Altered brain glymphatic flow at diffusion‐tensor MRI in rapid eye movement sleep behavior disorder. Radiology. 2023;307(5):e221848. doi: 10.1148/radiol.221848 [DOI] [PubMed] [Google Scholar]
- 18. Si X, Guo T, Wang Z, et al. Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson's disease. NPJ Parkinsons Dis. 2022;8(1):54. doi: 10.1038/s41531-022-00316-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Cai X, Chen Z, He C, et al. Diffusion along perivascular spaces provides evidence interlinking compromised glymphatic function with aging in Parkinson's disease. CNS Neurosci Ther. 2023;29(1):111‐121. doi: 10.1111/cns.13984 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Meng JC, Shen MQ, Lu YL, et al. Correlation of glymphatic system abnormalities with Parkinson's disease progression: a clinical study based on non‐invasive fMRI. J Neurol. 2023;271:457‐471. doi: 10.1007/s00415-023-12004-6 [DOI] [PubMed] [Google Scholar]
- 21. Ruan X, Huang X, Li Y, Li E, Li M, Wei X. Diffusion tensor imaging analysis along the perivascular space index in primary Parkinson's disease patients with and without freezing of gait. Neuroscience. 2022;506:51‐57. doi: 10.1016/j.neuroscience.2022.10.013 [DOI] [PubMed] [Google Scholar]
- 22. He P, Shi L, Li Y, et al. The association of the glymphatic function with parkinson's disease symptoms: neuroimaging evidence from longitudinal and cross‐sectional studies. Ann Neurol. 2023;94(4):672‐683. doi: 10.1002/ana.26729 [DOI] [PubMed] [Google Scholar]
- 23. He P, Gao Y, Shi L, et al. Motor progression phenotypes in early‐stage Parkinson's disease: a clinical prediction model and the role of glymphatic system imaging biomarkers. Neurosci Lett. 2023;814:137435. doi: 10.1016/j.neulet.2023.137435 [DOI] [PubMed] [Google Scholar]
- 24. Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015;30(12):1591‐1601. doi: 10.1002/mds.26424 [DOI] [PubMed] [Google Scholar]
- 25. Zhou C, Wang L, Cheng W, et al. Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease. NPJ Parkinsons Dis. 2023;9(1):111. doi: 10.1038/s41531-023-00556-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. De Pablo‐Fernández E, Lees AJ, Holton JL, Warner TT. Prognosis and neuropathologic correlation of clinical subtypes of Parkinson disease. JAMA Neurol. 2019;76(4):470‐479. doi: 10.1001/jamaneurol.2018.4377 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. O'Sullivan SS, Massey LA, Williams DR, et al. Clinical outcomes of progressive supranuclear palsy and multiple system atrophy. Brain. 2008;131(Pt 5):1362‐1372. doi: 10.1093/brain/awn065 [DOI] [PubMed] [Google Scholar]
- 28. Xiao S, Lewis M, Mellor D, et al. The China longitudinal ageing study: overview of the demographic, psychosocial and cognitive data of the Shanghai sample. J Ment Health. 2016;25(2):131‐136. doi: 10.3109/09638237.2015.1124385 [DOI] [PubMed] [Google Scholar]
- 29. Dubois B, Burn D, Goetz C, et al. Diagnostic procedures for Parkinson's disease dementia: recommendations from the Movement Disorder Society Task Force. Mov Disord. 2007;22(16):2314‐2324. doi: 10.1002/mds.21844 [DOI] [PubMed] [Google Scholar]
- 30. Siow TY, Toh CH, Hsu JL, et al. Association of sleep, neuropsychological performance, and gray matter volume with glymphatic function in community‐dwelling older adults. Neurology. 2022;98(8):e829‐e838. doi: 10.1212/wnl.0000000000013215 [DOI] [PubMed] [Google Scholar]
- 31. Huang SY, Zhang YR, Guo Y, et al. Glymphatic system dysfunction predicts amyloid deposition, neurodegeneration, and clinical progression in Alzheimer's disease. Alzheimers Dement. 2024;20:3251‐3269. doi: 10.1002/alz.13789 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Bäckström D, Granåsen G, Domellöf ME, et al. Early predictors of mortality in parkinsonism and Parkinson disease: a population‐based study. Neurology. 2018;91(22):e2045‐e2056. doi: 10.1212/wnl.0000000000006576 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Zou W, Pu T, Feng W, et al. Blocking meningeal lymphatic drainage aggravates Parkinson's disease‐like pathology in mice overexpressing mutated α‐synuclein. Transl Neurodegener. 2019;8:7. doi: 10.1186/s40035-019-0147-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Liu S, Sun X, Ren Q, et al. Glymphatic dysfunction in patients with early‐stage amyotrophic lateral sclerosis. Brain. 2023;147:100‐108. doi: 10.1093/brain/awad274 [DOI] [PubMed] [Google Scholar]
- 35. Carotenuto A, Cacciaguerra L, Pagani E, Preziosa P, Filippi M, Rocca MA. Glymphatic system impairment in multiple sclerosis: relation with brain damage and disability. Brain. 2022;145(8):2785‐2795. doi: 10.1093/brain/awab454 [DOI] [PubMed] [Google Scholar]
- 36. Zhang X, Wang Y, Jiao B, et al. Glymphatic system impairment in Alzheimer's disease: associations with perivascular space volume and cognitive function. Eur Radiol. 2023;34:1314‐1323. doi: 10.1007/s00330-023-10122-3 [DOI] [PubMed] [Google Scholar]
- 37. Qin Y, He R, Chen J, et al. Neuroimaging uncovers distinct relationships of glymphatic dysfunction and motor symptoms in Parkinson's disease. J Neurol. 2023;270(5):2649‐2658. doi: 10.1007/s00415-023-11594-5 [DOI] [PubMed] [Google Scholar]
- 38. Gu L, Dai S, Guo T, et al. Noninvasive neuroimaging provides evidence for deterioration of the glymphatic system in Parkinson's disease relative to essential tremor. Parkinsonism Relat Disord. 2023;107:105254. doi: 10.1016/j.parkreldis.2022.105254 [DOI] [PubMed] [Google Scholar]
- 39. Matrongolo MJ, Ang PS, Wu J, et al. Piezo1 agonist restores meningeal lymphatic vessels, drainage, and brain‐CSF perfusion in craniosynostosis and aged mice. J Clin Invest. 2023;134(4):e17148. doi: 10.1172/jci171468 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Xie L, Kang H, Xu Q, et al. Sleep drives metabolite clearance from the adult brain. Science (New York, NY). 2013;342(6156):373‐377. doi: 10.1126/science.1241224 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Wood KH, Nenert R, Miften AM, et al. Diffusion tensor imaging‐along the perivascular‐space index is associated with disease progression in Parkinson's disease. Mov Disord. 2024;39:1504‐1513. doi: 10.1002/mds.29908 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1.
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.