Neuroimaging-Based Glymphatic Function Predicts Conversion from Prodromal to Manifest Parkinson's Disease

Neuroimaging-Based Glymphatic Function Predicts Conversion from Prodromal to Manifest Parkinson's Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neuroimaging-Based Glymphatic Function Predicts Conversion from Prodromal to Manifest Parkinson's Disease Amei Chen, Zhanyu Kuang, Pek-Lan KHONG, Junxiang Huang, Jinyu Wen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5827541/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated glymphatic system integrity in prodromal Parkinson’s disease (pPD) using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) technique, analyzing data from 51 healthy controls, 83 pPD individuals, and 202 de novo Parkinson’s disease (dnPD) patients. The DTI-ALPS index was significantly reduced in both pPD and dnPD groups compared to controls and correlated with anxiety scores and cerebrospinal fluid tau levels in the pPD group. Longitudinal analysis revealed that a higher DTI-ALPS index was associated with a lower risk of conversion from pPD to clinical PD, suggesting that glymphatic dysfunction in pPD may serve as a predictive biomarker for disease progression. Biological sciences/Neuroscience Health sciences/Neurology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by the pathological accumulation of alpha-synuclein and the depletion of dopaminergic neurons in the substantia nigra (SN).. [ 1 , 2 ] While the clinical diagnosis of PD primarily relies on the presence of motor symptoms, the underlying pathological changes and neurodegenerative processes actually initiate years before the onset of these cardinal motor manifestations, during what is known as the prodromal phase of Parkinson's disease (pPD). [ 3 , 4 ] Characterized predominantly by subtle non-motor symptoms, pPD often goes unnoticed in its early stages, posing challenges for early detection. [ 5 ] There is a critical need for robust biomarkers that can facilitate the early and precise diagnosis of pPD and identify those at risk of progressing to PD. The identification of such individuals is paramount for the timely implementation of neuroprotective strategies, which are essential for maximizing therapeutic efficacy. The glymphatic system of the brain, discovered in recent years, is a highly organized waste removal pathway that removes soluble proteins, including amyloid-beta (Aβ) and tau, from the brain. [ 6 , 7 ] Emerging evidence suggests that dysfunctions in the glymphatic system's ability to clear these waste products may contribute to the onset and progression of various neurodegenerative disorders, including Alzheimer's disease (AD). [ 8 ] In a seminal study, Zou and colleagues demonstrated through animal models that the abnormal accumulation of α-synuclein resulting from impaired glymphatic function is a pivotal factor in the pathogenesis of PD. [ 9 ] A previous study investigated brain lymphoid system dysfunction in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), which is considered a prodromal Parkinson's disease. [ 10 ] Longitudinal research has further revealed that diminished glymphatic system function in iRBD is indicative of a heightened risk for the development of Parkinson's disease. [ 11 , 12 ] However, iRBD is only one specific type of pPD, and its pathologic development pattern may differ from other types. [ 13 , 14 ] Therefore, the value of altered brain glymphatic system function should be validated in a mixed pPD population . The initial techniques for assessing the glymphatic system involved the intrathecal injection of gadolinium-based contrast agents (GBCA) as tracers. [ 15 , 16 ] An alternative method involved intravenous injection of GBCA to monitor the enhancement of the interstitial space surrounding blood vessels as an indicator of glymphatic system function. [ 17 ] But all of these methods are invasive.Recently, diffusion tensor image analysis along the perivascular space (DTI-ALPS) has emerged as a non-invasive, real-time technique for evaluating the glymphatic system. [ 18 ] DTI-ALPS has demonstrated a significant correlation with glymphatic clearance function as assessed by intrathecal vascular tracing methods [ 19 ] and has proven to be a reliable approach, consistent across different MRI scanners. [ 20 , 21 ] Currently, DTI-ALPS is the most extensively utilized MRI-based technique for evaluating human glymphatic function and has been applied in the study of various diseases, including Alzheimer's disease, type 2 diabetes, ischemic stroke, multiple sclerosis, and Parkinson's disease . [ 22 – 27 ] In this study, DTI-ALPS index was used to compare the glymphatic function in pPD, PD and healthy controls, and the relationship between DTI-ALPS and clinical features and cerebrospinal fluid biological markers in pPD group was analyzed. In addition, we tried to explore the relationship between the function of the glymphatic system and the volume of gray matter in pPD. Then, in a follow-up study, we assessed the association of DTI-ALPS index with the probability of conversion from pPD to clinical PD. Results Demographics and clinical characteristics A total of 51 cases of HCs, 83 cases of pPD and 202 cases of dnPD were included in this study. The demographic and clinical characteristics of these participants at baseline were shown in Table 1 (Table 1). No significant differences were observed in age and sex among the three groups. However, there were statistically significant differences in years of education, UPSIT scores, RBDSQ scores, SCOPA-AUT scores, UPDRS III scores, and in the distribution of GBA and LRRK2 genotypes. Inter-observer consistency in the DTI-ALPS index Two independent observers, blinded to each other’s results, performed the DTI-ALPS index measurements on a subset of 30 participants from our study cohort. The intra-class correlation coefficient (ICC) was found to be 0.85(95% confidence interval [CI]: 0.75-0.91) , indicating an excellent level of agreement between observers. Difference of DTI-ALPS index among groups The mean, left and right DTI-ALPS index were all significantly different among the three groups ( P < 0.001). The subsequent tests showed that the mean, left and right DTI-ALPS index in the pPD group and the PD group was significantly lower than that in the HC group, but the difference in the mean, left and right DTI-ALPS index between the pPD group and the dnPD group was not statistically significant.(Fig.1). Correlation between DTI-ALPS index and clinical features In the pPD group, after adjusted for age, sex, and years of education,DTI-ALPS index was significantly negatively correlated with STAI score (r=0.46, P =0.02;). and it was positively correlated with p-tau concentrations (r=0.52, P =0.04) and t-tau concentrations (r=0.42, P =0.03). (Fig.2)We did not find significant associations between DTI-ALPS and clinical or genetic variables such as STAI scores, RBDSQ scores, GBA, and LRRK2. (Supplementary Table 1) Association between DTI-ALPS index and gray matter volume After adjusting for age, sex, and total intracranial volume, significant positive associations were observed between DTI-ALPS index and gray matter volumes of right temporal pole (FDR-adjusted P = 0.03),left thalamus(FDR-adjusted P = 0.01), right superior occipital gyrus (FDR-adjusted P = 0.04),and significant negative associations were observed between DTI-ALPS index and the gray matter volume of left posterior central gyrus(FDR-adjusted P = 0.03)(Fig.3). Influence of DTI-ALPS on the conversion of pPD to PD Among 83 prodromal PD subjects, 10 (12.1%) converted to clinical PD with median follow-up time 5.48 years. Table 2 showed the baseline characteristics of risk factors in pPD patients with PD conversion versus without PD conversion during the follow-up period, and the results revealed that the frequency is higher in RBD patients, and patients with anxiety.(Due to the substantial missing data in DAT-SPECT, it was not included in the statistics.) Univariate Cox regression identified five variables associated with PD conversion (p < 0.1): baseline DTI-ALPS index , RBDSQ , STAI score,GBA and LRRK2 status (Table 1). LASSO penalization further refined the model by excluding GBA and LRRK2 status (coefficient shrunk to zero). The optimal lambdaλ value (3.402) retained three predictors: DTI-ALPS index ,RBDSQ score and STAI score (Supplementary Fig. 1). The subsequent multifactor COX regression model showed that lower baseline DTI-ALPS index (HR=0.87, P = 0.004), higher RBDSQ score (HR=1.83, P = 0.016) and higher STAI score (HR=1.29, P = 0.027) independently predicted PD conversion (Table 3). Kaplan-Meier survival curves revealed that a higher DTI-ALPS index was associated with a lower progression rate from pPD to clinical PD ( P = 0.008).(Fig.4) Multi-model COX regression analysis showed that DTI-ALPS is an independent predictor of conversion from pPD to PD（Model 1：HR = 0.84, 95% CI: 0.81-0.90, P = 0.012，Model 2：HR = 0.84, 95% CI: 0.82-0.90, P = 0.006，Model 3：HR = 0.87, 95% CI: 0.82-0.92, P = 0.013，Model 4：HR = 0.87, 95% CI: 0.83-0.92, P = 0.018）. Discussion This study explored the glymphatic system's function as a potential biomarker for the early detection of pPD and its capacity to predict the conversion of pPD to clinical PD, utilizing both cross-sectional and longitudinal data. We utilized the DTI-ALPS index to represent glymphatic system function, and the findings indicate that: (1) the DTI-ALPS index was significantly reduced in the pPD group compared to healthy controls; (2) the DTI-ALPS index was negatively associated with the STAI score and positively correlated with CSF p-tau and t-tau levels in the pPD group; (3) the DTI-ALPS index correlated with the cortical volume of the right temporal pole , left thalamus, right superior occipital gyrus and left posterior central gyrus ; (4) in the longitudinal follow-up, the DTI-ALPS index emerged as an independent predictor of pPD progression to PD. Our study suggests that the DTI-ALPS index may serve as a neuroimaging biomarker for early detection of pPD, and it further provides clinical evidence that dysfunction of the glymphatic system is a potential mechanism underlying the conversion of pPD to PD. This insight may aid in the development of novel therapeutic targets and inform more appropriate clinical intervention timing. DTI-ALPS is a recently proposed non-invasive measurement method that quantitatively reflects the function of the glymphatic system. DTI-ALPS has been validated in a previous human-based study,which demonstrated a significant correlation between the DTI-ALPS index and the glymphatic function calculated by classical intrathecal injection of contrast agent, indicating that the DTI-ALPS index can reflect the clearing function of the glymphatic system. [19] Additionally, the measurement of DTI-ALPS is simple and can yield results within a few minutes, allowing for the real-time reflection of glymphatic system function. [32] DTI-ALPS exhibits good stability, which has been confirmed in previous studies, and our research has also demonstrated good inter-observer consistency. Therefore, DTI-ALPS has been applied in the detection and monitoring of many clinical conditions including Alzheimer’s disease, hydrocephalus, diabetes,Parkinson’s disease and has shown a correlation with the severity of clinical diseases. [33-36] To the best of our knowledge, DTI-ALPS has not yet been applied to the detection of pPD, especially for longitudinal follow-up to predict the conversion of pPD to PD. We have observed that the DTI-ALPS index is significantly lower in the pPD group compared to HC. We know that pPD is the preclinical stage of PD, which usually lasts for decades and is often not recognized early due to its weak and subtle clinical symptoms. However, previous studies have shown that pathological changes occur long before the appearance of typical PD motor symptoms. [37] We speculated that the glymphatic system dysfunction had already occurred in PPD stage. A previous study on iRBD population also obtained similar results, showing that the DTI-ALPS index in the iRBD group was significantly lower than that in the HC group. [23] iRBD is a representative group of the general pPD population, and our PPD population contains 61.6% of iRBD patients, but our cohort may be more representative of the general pPD population. However, we also noted that there was no significant difference in DTI-ALPS index between the pPD and the PD group, which we analyzed may be due to the fact that the included PD population was mostly early PD patients, and compensatory mechanisms (such as AQP4 redistribution) may temporarily stabilize lymphatic clearance and mask further decline. [38] Our results showed that glymphatic function was negatively correlated with STAI score in the pPD group, after controlling for age, sex, and education. Previous surveys of non-motor symptoms in people with pPD have shown that anxiety is a very common neuropsychiatric symptom of pPD. [39] Another animal study on mice after sleep deprivation showed that mice with chronic sleep restriction had reduced glymphatic function and showed significant anxiety-like behavior, It suggests that dysfunction of The glymphatic system may play a role in the development of anxiety-like behaviors of mice after chronic sleep restriction. [40] Additionally, with regard to the CSF biomarkers, glymphatic function was positively correlated with p-Tau and t-Tau level in pPD ,which highlights a critical interplay between protein clearance mechanisms and neurodegenerative pathology. Tau, a microtubule-associated protein, is physiologically released into the extracellular space and subsequently cleared via the glymphatic system—a process dependent on astrocytic aquaporin-4 (AQP4) water channels that facilitate cerebrospinal-interstitial fluid exchange. [41] In PD, while α-synuclein aggregation is the primary hallmark, impaired glymphatic function disrupts the clearance of multiple neurotoxic proteins, including tau. Animal studies demonstrate that AQP4 deficiency reduces tau efflux from brain parenchyma to CSF, leading to its pathological accumulation. [42] Our findings align with this mechanism, showing that glymphatic dysfunction emerges as early as the pPD stage, preceding clinical PD onset. This dysfunction compromises tau clearance, potentially exacerbating α-synuclein aggregation through synergistic neurotoxicity or neuroinflammatory cascades. The observed correlations between reduced DTI-ALPS index and GMV alterations in the left postcentral gyrus, right superior occipital gyrus, right temporal pole, and left thalamus suggested a potential interplay between glymphatic dysfunction and region-specific neurodegeneration. The glymphatic system, regulated by AQP4 polarization and perivascular fluid dynamics, is critical for clearing neurotoxic proteins such as α-synuclein and amyloid-β, which accumulate in PD pathogenesis. [43] Impaired glymphatic activity, reflected by diminished DTI-ALPS index, may exacerbate protein aggregation, neuroinflammation, and subsequent GMV loss in metabolically vulnerable regions. The left thalamus, a hub for sensorimotor integration, exhibits high metabolic demand and is susceptible to impaired waste clearance, as evidenced by thalamic atrophy in neurodegenerative disorders linked to glymphatic failure. [44] Similarly, the right temporal pole, implicated in social-emotional processing, is an early site of α-synuclein pathology in PD, where glymphatic dysfunction may accelerate synaptic loss and structural decline. [12] The association with the left postcentral gyrus and right superior occipital gyrus highlights spatial specificity in glymphatic impairment. The postcentral gyrus, integral to somatosensory processing, may experience microstructural damage due to disrupted perivascular fluid exchange, akin to mechanisms observed in cerebral small vessel disease. [45] The superior occipital gyrus, involved in visual processing, could similarly undergo atrophy from impaired metabolite clearance, as glymphatic failure disrupts fluid homeostasis in regions with high interstitial solute production. [46] Future studies should validate these findings in longitudinal cohorts and explore therapeutic strategies targeting AQP4-mediated fluid transport to mitigate region-specific neurodegeneration. A recent longitudinal study of PD found that DTI-ALPS index was associated with PD symptom severity and significantly associated with faster PD disease progression,which suggested that DTI-ALPS index might be a biomarker for predicting the progression of PD disease. [47] In this study, we analyzed 4-year longitudinal follow-up data of PD in prodrome. In a multi-factor COX regression model, the baseline DTI-ALPS index level was an independent predictor from pPD conversion to PD. This finding is consistent with the results of previous studies and further expands the value of DTI-ALPS index in the diagnosis and prediction of PD. We can speculate that in the prodromal PD stage, glymphatic dysfunction has emerged, leading to the clearance of neurotoxic proteins (such as α-syn), which in turn causes abnormal aggregation of α-syn and promotes the progression of the disease to PD. DTI-ALPS index, as a non-invasive neuroimaging index, can help us identify high-risk patients in advance and provide an important basis for early diagnosis and treatment of PD. This provides important clues for the development of new therapeutic targets and the selection of better treatment timing. Our study had several limitations.First, the relationship between DTI-ALPS and human glymphatic function remains to be thoroughly and rigorously confirmed by pathophysiological studies. The DTI-ALPS , in addition to reflecting the diffusion rate along the perivascular space, may incorporate the contribution of vascular pulsation, tissue fluid dynamics, and tissue microstructure. Therefore, caution should be exercised when interpreting the association between the Alpine index and glymphatic clearance. [48] Second, it is currently unclear whether these pPD participants will ultimately develop PD as a result of short-term follow-up. This is a trap that has existed in all studies to date. Third, the follow-up analysis of pPD subjects had a small sample size and a relatively short follow-up time, and future studies will need a larger sample size and longer follow-up time.Fourth, the pPD subjects in this study were pPD population defined by PPMI, which was slightly different from International Parkinson's Disease and Movement Disorders Association (MDS) research criteria for pPD (MDS-pPD) [49] ,so the results need to be verified by the subjects who meet the MDS--pPD criteria in the future.Fifth, it is recommended that future studies include metabolic risk factors such as hypertension and diabetes. In summary, assessing brain glymphatic dysfunction in individuals with pPD using DTI-ALPS revealed glymphatic alterations. In individuals who phenotypically convert to PD, the reduction of glymphatic activity was more severe. Therefore, DTI-ALPS may contribute in identifying individuals with pPD at a high risk for conversion to PD, thus enabling earlier intervention and potentially more effective disease management. Methods Participant s This study analyzed data from participants with pPD, dnPD patients and healthy controls,de novo , all of whom were part of the Parkinson's Disease Progression Markers Initiative (PPMI). PPMI is a prospective, longitudinal, observational multicenter study aimed at validating biomarkers of PD progression. pPD patients were identified as individuals at risk for developing PD based on clinical characteristics, genetic variations, or other biomarkers, such as RBD, genetic risk variants (LRRK2, GBA), the University of Pennsylvania Smell Identification Test (UPSIT)-based hyposmia, and positive dopamine transporter (DAT) SPECT on visual inspection. Detailed criteria for participant inclusion are available online at ppmi-info.org/study-design. dnPD patients were drug-naive, newly diagnosed PD patients. Study visits were conducted at baseline, followed by assessments at 3-month intervals during the first year and biannually thereafter. To minimize confounding effects of pharmacotherapy, the pPD cohort strictly excluded individuals receiving PD-specific medications, consistent with the PPMI protocol. Data for this analysis were retrieved from the PPMI database in May 2023.Participant inclusion criteria were as follows:1. All participants were required to have high-quality 3D T1-weighted magnetic resonance imaging (MRI) and DTI datasets acquired at baseline. 2. To ensure homogeneity in imaging protocols, only data obtained from 3.0 Tesla Siemens MRI scanners (MAGNETOM Trio/TIM/Skyra/Vida systems) using harmonized acquisition parameters (e.g., repetition time, echo time, voxel resolution) were included. 3. The pPD cohort was restricted to individuals with a minimum follow-up duration of 4 years to enable robust assessment of conversion patterns. 4. Participants were excluded if MRI scans exhibited motion artifacts, field inhomogeneities, or acquisition errors that precluded quantitative analysis. PD converters were operationally defined as individuals meeting the Movement Disorder Society (MDS) 2015 Clinical Diagnostic Criteria for Parkinson's Disease [28] on two consecutive clinical evaluations. To enhance diagnostic specificity, two participants initially classified as PD converters were later excluded after re-evaluation confirmed alternative α-synucleinopathies (dementia with Lewy bodies and multiple system atrophy). Ethical approval The PPMI study is registered at Clinical Trials.gov (NCT01141023). Each participating PPMI site received approval from an ethical standards committee on human experimentation before the start of the study. Written informed consent for the study was obtained from all participating individuals. Clinical evaluation All participants underwent comprehensive clinical assessments at baseline using standardized scales to evaluate motor and non-motor symptoms. Motor symptoms were assessed with the Unified Parkinson’s Disease Rating Scale, Part III (UPDRS III). Non-motor assessments included: UPSIT ( 40-item odor identification, scores ≤ 35 suggesting hyposmia), RBDSQ (scores ≥6 indicating risk), the Epworth Sleepiness Scale (ESS; daytime sleepiness severity, ≥10 indicating excessive sleepiness), the Montreal Cognitive Assessment (MoCA; cognitive screening, scores ≤26 indicating impairment), the State-Trait Anxiety Inventory (STAI; 20-item state and 20-item trait anxiety subscales, higher scores reflecting greater anxiety), and the 15-item Geriatric Depression Scale (GDS-15; scores ≥5 suggesting depression). Autonomic dysfunction was evaluated using the Scale for Outcomes in Parkinson’s Disease-Autonomic (SCOPA-AUT; scores 0–69, higher scores indicating worse dysfunction). Further details on these scales, including validation and administration, are described in prior work. [29, 30] Image Acquisition Non-contrast enhanced 3D volumetric T1-weighted MRI and DTI scans of the total subjects were acquired on 3T Siemens MRI scanners (Erlangen, Germany) using an MPRAGE sequence at different centers. Acquisition parameters and detailed protocols are available on the PPMI website. The following indexes were included in the protocol: (1) DTI: 72 axial slices, echo time (TE) = 88 ms, repetition time (TR) = 500–9,000 ms, voxel size: 2.0 × 2.0 × 2.0 mm3 , acquisition matrix = 1,044 × 1,044; one diffusion-unweighted (b0) image and 64 diffusion-sensitive gradient directions at b = 1,000s/mm2 images; (2) 3D-T1WI: TE = 2.98 ms, TR = 2,300 ms, voxel size: 1.0×1.0×1.0 mm3 , acquisition matrix = 240 × 256. Staff at each center were trained to ensure that the data were collected in a standardized manner. In subsequent data analysis, subjects with missing data were excluded. DTI-ALPS index Processing We adopted the method for DTI-ALPS processing and measurement from the previous publication. [18] First, the diffusion weighted images were processed by FMRIB software (FSL, FSL/http://www.fmrib.ox.ac.uk/). The process includes the following steps: (1) We converted DTI images in raw data into 4D NIfTI format files using dcm2nii software. The head motion and eddy current of DTI image were corrected. (3)The skull of the scalp was removed to obtain brain tissue. (4)The dtifit command of FSL was used to reconstruct the tensor from the DTI image, the color fraction anisotropy (FA) image was obtained. At the level of the lateral ventricle, the direction of the subcortical fibers and the perivascular space is almost the same, both perpendicular to the lateral ventricle, mainly in the left and right direction, that is, the X-axis.The associated fibers are mainly in the forward and backward direction (y axis), while the projection fibers maintain the up and down direction (z axis). The directions of perivascular space, associated fibers and projective fibers are perpendicular to each other. Two trained neuroradiologists evaluated all images without knowledge of clinical data, independently selecting region of interest (ROI). A spherical ROI with a diameter of 4mm was placed on projective fibers, associated fibers, and subcortical fibers in the left and right hemispheres. Fsleyes software was used to extract x, y, z direction fusion fibers Dxx, Dyy and Dzz for projection and association fibers within ROI. The DTI-ALPS index for the left and right hemispheres was calculated separately, and then the average DTI-ALPS index for both sides was used for the main analysis. (Fig.5) The formula for calculating DTI-ALPS index is as follows: (1) Dxxproj: diffusivity along the x-axis in the projection fiber, Dxxassoci: diffusivity along the x-axis in the association fiber, Dyyproj: diffusivity along the y-axis in the projection fiber, Dzzassoci: diffusivity along the z-axis in the association fiber. Gray matter volume measurement Baseline brain MRI sequences were derived from the PPMI database.We applied a voxel-based morphometry method using high-resolution 3-D T1-weighted images. By affine and high-dimensional nonlinear registration, the individual image was spatially normalized to the standard stereotactic space,by using CAT12 toolbox (dbm.neuro.uni-jena.de/cat/) and MATLAB SPM12 (fil.ion.ucl.ac.uk/spm/software/ spm12/). We segmented MRI images into gray matter and white matter in the standard space of the Montreal Neurological Institute (MNI) in high dimension, and then segmented gray matter into different brain regions using the AAL3 template. Statistics Statistical analysis was performed by SPSS 26.0 and R statistical software (version 3.4.3; http://www.r-project.org/) . Statistical plots were generated using GraphPad Prism 8.0a (GraphPad Inc., San Diego, CA, USA). Normally distributed Continuous variables (assessed via Shapiro-Wilk test)were represented by mean ± standard deviation (SD). These included age, years of education, MoCA ,ESS scores and cerebrospinal fluid concentration of α-syn, t-tau, p-tau . Comparisons between groups were performed using one-way ANOVA . Non-normally distributed variables (skewed data) were represented by median (1st quartile, 3rd quartile). These included UPSIT, RBDSQ, SCOPA-AUT, STAI, UPDRS III and GDS-15 scores. Comparisons between groups were performed using the Kruskal-Wallis test. Categorical Variables reported as percentage. These included sex, GBA status, and LRRK status. Comparisons between groups were performed using the chi-square test or Fisher’s exact test (for cells with expected counts <5). Bonferroni correction was applied to adjust for multiple comparisons. Missing values (<5% of total data) were imputed using a K-Nearest Neighbor (KNN) weighted imputation method (k=10 neighbors, Euclidean distance metric).A two-tailed p-value <0.05 was considered statistically significant. In pPD group we performed a partial Pearson/Spearman correlation analysis with age, sex, and education as covariates(Education was included as a covariate due to its association with cognitive reserve, which may influence neurodegenerative progression [31] ) to identify the relationship between DTI-ALPS index and gray matter volume, as well as the relationship between DTI-ALPS index and various clinical features. Baseline variables with potential prognostic relevance were first screened using univariate Cox proportional hazards models. Variables with a significance level of P < 0.1 were retained for further analysis to avoid premature exclusion of weakly associated predictors. To address overfitting and select the most parsimonious set of predictors, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was applied to the retained variables. The optimal tuning parameter (lambda λ) was determined by minimizing the partial likelihood deviance. Variables selected by LASSO were subsequently incorporated into a multivariable Cox proportional hazards model to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). The influence of DTI-ALPS index levels on pPD to PD conversion was examined by Kaplan-Meier survival analysis,with differences between groups evaluated through the log-rank test.Double-tail test was used, and P < 0.05 was considered statistically significant. Sensitivity analysis was performed by multi-model cox regression analysis to examine the robustness of our results.Model 1 is crude. Model 2 includes three confounding factors:sex, age, education（Education was included as a covariate due to its association with cognitive reserve, which may influence neurodegenerative progression). [31] Model 3 was adjusted for sex, age, education, RBDSQ, STAI_anxiety_total scores. Model 4 was further adjusted for sex, age, education, RBDSQ, STAI_anxiety_total scores,GBA_PATHVAR, LRRK2_PATHVAR. Hazard ratios (HRs) with 95% confidence intervals (Cis) were estimated to determine the risk of PD conversion. Declarations Financial Disclosure: There is no Conflict of Interest concerning the research related to the manuscript. Data Availability Statement Data used in the preparation of this study were obtained from the Parkinson’s Progression Markers Initiative database. All data are full access and available at www.ppmi-info.org. Acknowledgments This work was supported by grants from the Guangzhou Science and Technology Project(2024A03J1109) and research project of Chinese Medicine Bureau of Guangdong Province(20251277). The authors gratefully thank all study participants for their participation in this study. Author contributions XW : conceived and designed the experiments. ZK and JH: contributed signifcantly to the experiments. JW,YZ:arranging data and performing data analyses. AC: wrote the draft manuscript. PK and YC, revised the manuscript. 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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. Zhang W, Zhou Y, Wang J, et al. Glymphatic clearance function in patients with cerebral small vessel disease. Neuroimage. 2021. 238: 118257. Taoka T, Ito R, Nakamichi R, et al. Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study. Jpn J Radiol. 2022. 40(2): 147-158. Liu X, Barisano G, Shao X, et al. Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function. Aging Dis. 2023 . Lee HJ, Lee DA, Shin KJ, Park KM. Glymphatic system dysfunction in obstructive sleep apnea evidenced by DTI-ALPS. Sleep Med. 2022. 89: 176-181. 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. Yang G, Deng N, Liu Y, Gu Y, Yao X. Evaluation of Glymphatic System Using Diffusion MR Technique in T2DM Cases. Front Hum Neurosci. 2020. 14: 300. Chen HL, Chen PC, Lu CH, et al. Associations among Cognitive Functions, Plasma DNA, and Diffusion Tensor Image along the Perivascular Space (DTI-ALPS) in Patients with Parkinson's Disease. Oxid Med Cell Longev. 2021. 2021: 4034509. 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. Toh CH, Siow TY. Glymphatic Dysfunction in Patients With Ischemic Stroke. Front Aging Neurosci. 2021. 13: 756249. Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015. 30(12): 1591-601. Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease, . The Unified Parkinson's Disease Rating Scale (UPDRS): status and recommendations. Mov Disord. 2003. 18(7): 738-50. Brumm MC, Pierz KA, Lafontant DE, et al. Updated Percentiles for the University of Pennsylvania Smell Identification Test in Adults 50 Years of Age and Older. Neurology. 2023. 100(16): e1691-e1701. Joannette M, Bocti C, Dupont PS, et al. Education as a Moderator of the Relationship Between Episodic Memory and Amyloid Load in Normal Aging. J Gerontol A Biol Sci Med Sci. 2020. 75(10): 1820-1826. Taoka T, Ito R, Nakamichi R, Nakane T, Kawai H, Naganawa S. Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method. Magn Reson Med Sci. 2024. 23(3): 268-290. Chen H, Wan H, Zhang M, Wardlaw JM, Feng T, Wang Y. Perivascular space in Parkinson's disease: Association with CSF amyloid/tau and cognitive decline. Parkinsonism Relat Disord. 2022. 95: 70-76. Taoka T, Ito R, Nakamichi R, et al. Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study. Jpn J Radiol. 2021 . Shen T, Yue Y, Zhao S, et al. The role of brain perivascular space burden in early-stage Parkinson's disease. NPJ Parkinsons Dis. 2021. 7(1): 12. Kiviniemi V, Wang X, Korhonen V, et al. Ultra-fast magnetic resonance encephalography of physiological brain activity - Glymphatic pulsation mechanisms. J Cereb Blood Flow Metab. 2016. 36(6): 1033-45. Jankovic J. Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008. 79(4): 368-76. Shentu W, Kong Q, Zhang Y, et al. Functional abnormalities of the glymphatic system in cognitive disorders. Neural Regen Res. 2025. 20(12): 3430-3447. Schrag A, Bohlken J, Dammertz L, et al. Widening the Spectrum of Risk Factors, Comorbidities, and Prodromal Features of Parkinson Disease. JAMA Neurol. 2023. 80(2): 161-171. Zhai S, Yin MM, Sun HQ, et al. The day-night differences in cognitive and anxiety-like behaviors of mice after chronic sleep restriction. FASEB J. 2023. 37(7): e23034. Yue Y, Zhang X, Lv W, Lai HY, Shen T. Interplay between the glymphatic system and neurotoxic proteins in Parkinson's disease and related disorders: current knowledge and future directions. Neural Regen Res. 2024. 19(9): 1973-1980. Ishida K, Yamada K. Detection of Glymphatic Outflow of Tau from Brain to Cerebrospinal Fluid in Mice. Methods Mol Biol. 2024. 2754: 351-359. Bojarskaite L, Nafari S, Ravnanger AK, et al. Role of aquaporin-4 polarization in extracellular solute clearance. Fluids Barriers CNS. 2024. 21(1): 28. Gouveia-Freitas K, Bastos-Leite AJ. Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology. Neuroradiology. 2021. 63(10): 1581-1597. Reiländer A, Engel M, Nöth U, et al. Cortical microstructural involvement in cerebral small vessel disease. Cereb Circ Cogn Behav. 2024. 6: 100218. Wu W, Zhao Y, Cheng X, et al. Modulation of glymphatic system by visual circuit activation alleviates memory impairment and apathy in a mouse model of Alzheimer's disease. Nat Commun. 2025. 16(1): 63. 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. Ringstad G. Glymphatic imaging: a critical look at the DTI-ALPS index. Neuroradiology. 2024. 66(2): 157-160. Berg D, Postuma RB, Adler CH, et al. MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2015. 30(12): 1600-11. Tables Tables 1-4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementaryfile.pdf Table1.docx Table2.docx Table3.docx Table4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5827541","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449257332,"identity":"7e80cb3b-93c1-4415-ab1a-9a1d64cc6145","order_by":0,"name":"Amei Chen","email":"","orcid":"","institution":"Guangzhou First People's Hospital Affiliated to Guangzhou Medical University，the Second Affiliated Hospital of South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Amei","middleName":"","lastName":"Chen","suffix":""},{"id":449257333,"identity":"31cfea6e-b5c5-4fc8-9a59-986ad32f0999","order_by":1,"name":"Zhanyu Kuang","email":"","orcid":"","institution":"Guangzhou First People's Hospital Affiliated to Guangzhou Medical University，the Second Affiliated Hospital of South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhanyu","middleName":"","lastName":"Kuang","suffix":""},{"id":449257334,"identity":"90eebb38-ac54-43eb-9b11-0e719d6ebd21","order_by":2,"name":"Pek-Lan KHONG","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Pek-Lan","middleName":"","lastName":"KHONG","suffix":""},{"id":449257335,"identity":"19d2365a-35b1-46ec-a8cb-4e41223e02e4","order_by":3,"name":"Junxiang Huang","email":"","orcid":"","institution":"Guangzhou Women and Children's Medical Center, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junxiang","middleName":"","lastName":"Huang","suffix":""},{"id":449257336,"identity":"7a312502-2692-4090-9c3a-14d67b923404","order_by":4,"name":"Jinyu Wen","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jinyu","middleName":"","lastName":"Wen","suffix":""},{"id":449257337,"identity":"a0c7dc3f-b49a-47af-8b72-6031ec61858e","order_by":5,"name":"Yoon Seong CHOI","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Yoon","middleName":"Seong","lastName":"CHOI","suffix":""},{"id":449257338,"identity":"635b5535-768d-47e5-b400-5b6d7f3ee579","order_by":6,"name":"Yongzhou Xu","email":"","orcid":"","institution":"Philips Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Yongzhou","middleName":"","lastName":"Xu","suffix":""},{"id":449257340,"identity":"e777f46f-6d53-4b9b-88d2-2580adf26174","order_by":7,"name":"Xinhua Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYJCCA4wNIIqx8UFCRQ1pWpoNHpw5RqQ1EC0MbJIPW5gJq+aXyDE8XLjjsLzB8ea2isQGNgb+9u4EvFokZ6QlHJ555rDhhjMH224k7pBhkDhzdgNeLQY3kg8c5m07zLjtRiJQyxk2BgOJXPxa7G8kNoC02G+7/7CtILGNmbAWAwmILYnbbjC2MRClReLMswSglvTk/WcSmyUSzhzjIegX/vYc48+8bda2M9uPP/z4o6JGjr+9F78WBoEEVD4PfuVgaw4QVjMKRsEoGAUjHAAAoY9SXy2JChUAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou First People's Hospital Affiliated to Guangzhou Medical University，the Second Affiliated Hospital of South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xinhua","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-01-14 13:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5827541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5827541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82048706,"identity":"6e806ff2-9cdf-4ae5-b6c4-e0d47def29e2","added_by":"auto","created_at":"2025-05-06 09:47:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRaincloud plot of the DTI-ALPS (A),DTI-ALPS_L(B), DTI-ALPS_R(C) among the HC, pPD and dnPD. \u003c/strong\u003eHC: healthy controls, pPD : prodromal parkinson’s disease, dnPD: de novo parkinson’s disease. \u0026nbsp;APLS:diffusion tensor image analysis along the perivascular space. Error bars represent the standard error. **: P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"FIG.1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/58023ebd7e42ea87e962ebca.jpg"},{"id":82051645,"identity":"4900b928-5d08-4136-9f8d-ec9cc164b94d","added_by":"auto","created_at":"2025-05-06 10:03:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between the DTI-ALPS index and Clinical features.\u003c/strong\u003e There was a significant negative correlation between ALPS index and STAI-anxiety total scores (r = 0.46, P = 0.02) (A) , There was a significant positive correlation between DTI-ALPS index and p-Tau (r = 0.52, P = 0.04)(B)and t-tau (r =0.42, P = 0.03)(C) .\u003c/p\u003e","description":"","filename":"FIG.2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/a3cdfd751f60386cd3d1e2e9.jpg"},{"id":82048704,"identity":"dc4468bc-f244-40fa-8013-489beafca5a8","added_by":"auto","created_at":"2025-05-06 09:47:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between DTI-ALPS index and regional gray matter volume.\u003c/strong\u003e Illustration of the regions-of-interest (defined in AAL3) in which the regional gray matter volumes are significantly associated with DTI-ALPS index.Areas marked in red represent positive correlations, including right temporal pole , left thalamus, right superior occipital gyrus.Areas marked in blue represent positive correlations, including left posterior central gyrus.DTI-ALPS indicates diffusion tensor imaging analysis along the perivascular space.\u003c/p\u003e","description":"","filename":"FIG.3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/71e004003671bbbd55d0dee5.jpg"},{"id":82048700,"identity":"67321189-d168-422c-8f7b-65c20c3575a4","added_by":"auto","created_at":"2025-05-06 09:47:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97518,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves revealed higher DTI-ALPS index level showing a lower rate of progression from pPD to PD.\u003c/p\u003e","description":"","filename":"FIG.4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/373f92da53ca36f850fcc7ac.jpg"},{"id":82050397,"identity":"614e6cd4-24ba-4863-bb9b-b894c90bc4d1","added_by":"auto","created_at":"2025-05-06 09:55:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic drawing of the DTI-ALPS methodology.\u003c/strong\u003e a colored FA map shows the ROIs on the projection fibers (blue), association fibers (green), and subcortical fibers (red). b Schematic drawing of the spatial relationships between the perivascular space and subcortical fibers (red; x-axis), association fibers (green; y-axis), and projection fibers (blue; z-axis)\u003c/p\u003e","description":"","filename":"FIG.5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/7a426153bd63ba6104c9294f.jpg"},{"id":84626472,"identity":"fe1b2a05-0fa7-44a1-a3c5-2190099f4508","added_by":"auto","created_at":"2025-06-15 06:02:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1847606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/5a1c91b8-b395-4e0d-8ed4-69d9afc00dd8.pdf"},{"id":82053350,"identity":"4525438f-5575-4fc2-a500-3669b8387f04","added_by":"auto","created_at":"2025-05-06 10:11:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":179153,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/e568c633c877b43c0b6875ea.pdf"},{"id":82048697,"identity":"0dd793e8-66d1-46f2-beee-cc7c7b78f9e2","added_by":"auto","created_at":"2025-05-06 09:47:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20032,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/8a290956d2e0e8c25f9bdef7.docx"},{"id":82050395,"identity":"e0011f93-085d-4505-b3b2-4ca8fe84be92","added_by":"auto","created_at":"2025-05-06 09:55:12","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15427,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/217d382df1203056164124ac.docx"},{"id":82048699,"identity":"b44b652a-4623-4f1f-8b02-1d98f91df160","added_by":"auto","created_at":"2025-05-06 09:47:12","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":25202,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/66666ff4b0117475ca357ea3.docx"},{"id":82051646,"identity":"45e2468b-7910-4bbc-ac7a-379a6470f821","added_by":"auto","created_at":"2025-05-06 10:03:13","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13216,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5827541/v1/4f005511af88a03d774e150f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuroimaging-Based Glymphatic Function Predicts Conversion from Prodromal to Manifest Parkinson's Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson's disease (PD) is a progressive neurodegenerative disorder marked by the pathological accumulation of alpha-synuclein and the depletion of dopaminergic neurons in the substantia nigra (SN)..\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e While the clinical diagnosis of PD primarily relies on the presence of motor symptoms, the underlying pathological changes and neurodegenerative processes actually initiate years before the onset of these cardinal motor manifestations, during what is known as the prodromal phase of Parkinson's disease (pPD).\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e Characterized predominantly by subtle non-motor symptoms, pPD often goes unnoticed in its early stages, posing challenges for early detection.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e There is a critical need for robust biomarkers that can facilitate the early and precise diagnosis of pPD and identify those at risk of progressing to PD. The identification of such individuals is paramount for the timely implementation of neuroprotective strategies, which are essential for maximizing therapeutic efficacy.\u003c/p\u003e \u003cp\u003eThe glymphatic system of the brain, discovered in recent years, is a highly organized waste removal pathway that removes soluble proteins, including amyloid-beta (Aβ) and tau, from the brain. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003eEmerging evidence suggests that dysfunctions in the glymphatic system's ability to clear these waste products may contribute to the onset and progression of various neurodegenerative disorders, including Alzheimer's disease (AD).\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e In a seminal study, Zou and colleagues demonstrated through animal models that the abnormal accumulation of α-synuclein resulting from impaired glymphatic function is a pivotal factor in the pathogenesis of PD.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA previous study investigated brain lymphoid system dysfunction in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), which is considered a prodromal Parkinson's disease. \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003eLongitudinal research has further revealed that diminished glymphatic system function in iRBD is indicative of a heightened risk for the development of Parkinson's disease. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003eHowever, iRBD is only one specific type of pPD, and its pathologic development pattern may differ from other types.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e Therefore, the value of altered brain glymphatic system function should be validated in a mixed pPD population .\u003c/p\u003e \u003cp\u003eThe initial techniques for assessing the glymphatic system involved the intrathecal injection of gadolinium-based contrast agents (GBCA) as tracers. \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003eAn alternative method involved intravenous injection of GBCA to monitor the enhancement of the interstitial space surrounding blood vessels as an indicator of glymphatic system function.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e But all of these methods are invasive.Recently, diffusion tensor image analysis along the perivascular space (DTI-ALPS) has emerged as a non-invasive, real-time technique for evaluating the glymphatic system. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003eDTI-ALPS has demonstrated a significant correlation with glymphatic clearance function as assessed by intrathecal vascular tracing methods \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eand has proven to be a reliable approach, consistent across different MRI scanners.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eCurrently, DTI-ALPS is the most extensively utilized MRI-based technique for evaluating human glymphatic function and has been applied in the study of various diseases, including Alzheimer's disease, type 2 diabetes, ischemic stroke, multiple sclerosis, and Parkinson's disease .\u003csup\u003e[\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this study, DTI-ALPS index was used to compare the glymphatic function in pPD, PD and healthy controls, and the relationship between DTI-ALPS and clinical features and cerebrospinal fluid biological markers in pPD group was analyzed. In addition, we tried to explore the relationship between the function of the glymphatic system and the volume of gray matter in pPD. Then, in a follow-up study, we assessed the association of DTI-ALPS index with the probability of conversion from pPD to clinical PD.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 51 cases of HCs, 83 cases of pPD and 202 cases of dnPD were included in this study. The demographic and clinical characteristics of these participants at baseline were shown in Table 1 (Table 1). No significant differences were observed in age and sex among the three groups. However, there were statistically significant differences in years of education, UPSIT scores, RBDSQ scores, SCOPA-AUT scores, UPDRS III scores, and in the distribution of GBA and LRRK2 genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-observer consistency in the DTI-ALPS index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo independent observers, blinded to each other’s results, performed the DTI-ALPS index measurements on a subset of 30 participants from our study cohort. The intra-class correlation coefficient (ICC) was found to be 0.85(95% confidence interval [CI]: 0.75-0.91) , indicating an excellent level of agreement between observers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifference of DTI-ALPS index among groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean, left and right DTI-ALPS index were all significantly different among the three groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). The subsequent tests showed that the mean, left and right DTI-ALPS index in the pPD group and the PD group was significantly lower than that in the HC group, but the difference in the mean, left and right DTI-ALPS index between the pPD group and the dnPD group was not statistically significant.(Fig.1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between DTI-ALPS index and clinical features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the pPD group, after adjusted for age, sex, and years of education,DTI-ALPS index was significantly negatively correlated with STAI score \u0026nbsp;(r=0.46, \u003cem\u003eP\u003c/em\u003e=0.02;). and it was positively correlated with p-tau concentrations (r=0.52, \u003cem\u003eP\u003c/em\u003e=0.04) and t-tau concentrations (r=0.42, \u003cem\u003eP\u003c/em\u003e=0.03). (Fig.2)We did not find significant associations between DTI-ALPS and clinical or genetic variables such as STAI scores, RBDSQ scores, GBA, and LRRK2. (Supplementary Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between DTI-ALPS index and gray matter volume\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusting for age, sex, and total intracranial volume, significant positive associations were observed between DTI-ALPS index and gray matter volumes of right temporal pole (FDR-adjusted \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.03),left thalamus(FDR-adjusted \u003cem\u003eP\u003c/em\u003e = 0.01),\u0026nbsp;\u0026nbsp;right superior occipital gyrus (FDR-adjusted \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.04),and significant negative associations were observed between DTI-ALPS index and the gray matter volume of left posterior central gyrus(FDR-adjusted \u003cem\u003eP\u003c/em\u003e = 0.03)(Fig.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of DTI-ALPS on the conversion of pPD to PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 83 prodromal PD subjects, 10 (12.1%) converted to clinical PD with median follow-up time 5.48 years. Table 2 showed the baseline characteristics of risk factors in pPD patients with PD conversion versus without PD conversion during the follow-up period, and the results revealed that the frequency is higher in RBD patients, and patients with anxiety.(Due to the substantial missing data in DAT-SPECT, it was not included in the statistics.)\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression identified five variables associated with PD conversion (p \u0026lt; 0.1): baseline DTI-ALPS index , RBDSQ , STAI score,GBA and LRRK2 status (Table 1). LASSO penalization further refined the model by excluding GBA and LRRK2 status (coefficient shrunk to zero). The optimal lambdaλ value (3.402) retained three predictors: DTI-ALPS index ,RBDSQ score and STAI score (Supplementary Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe subsequent multifactor COX regression model showed that lower baseline DTI-ALPS index (HR=0.87, \u003cem\u003eP\u003c/em\u003e = 0.004), higher RBDSQ score (HR=1.83, \u003cem\u003eP\u003c/em\u003e = 0.016) and higher STAI score (HR=1.29, \u003cem\u003eP\u003c/em\u003e = 0.027) independently predicted PD conversion (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curves revealed that a higher DTI-ALPS index was associated with a lower progression rate from pPD to clinical PD (\u003cem\u003eP\u003c/em\u003e = 0.008).(Fig.4)\u003c/p\u003e\n\u003cp\u003eMulti-model COX regression analysis showed that DTI-ALPS is an independent predictor of conversion from pPD to PD（Model 1：HR = 0.84, 95% CI: 0.81-0.90,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.012，Model 2：HR = 0.84, 95% CI: 0.82-0.90,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.006，Model 3：HR = 0.87, 95% CI: 0.82-0.92,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.013，Model 4：HR = 0.87, 95% CI: 0.83-0.92,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.018）.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the glymphatic system\u0026apos;s function as a potential biomarker for the early detection of pPD and its capacity to predict the conversion of pPD to clinical PD, utilizing both cross-sectional and longitudinal data. We utilized the DTI-ALPS index to represent glymphatic system function, and the findings indicate that: (1) the DTI-ALPS index was significantly reduced in the pPD group compared to healthy controls; (2) the DTI-ALPS index was negatively associated with the STAI score and positively correlated with CSF p-tau and t-tau levels in the pPD group; (3) the DTI-ALPS index correlated with the cortical volume of the right temporal pole , left thalamus, right superior occipital gyrus and left posterior central gyrus ; (4) in the longitudinal follow-up, the DTI-ALPS index emerged as an independent predictor of pPD progression to PD. Our study suggests that the DTI-ALPS index may serve as a neuroimaging biomarker for early detection of pPD, and it further provides clinical evidence that dysfunction of the glymphatic system is a potential mechanism underlying the conversion of pPD to PD. This insight may aid in the development of novel therapeutic targets and inform more appropriate clinical intervention timing.\u003c/p\u003e\n\u003cp\u003eDTI-ALPS is a recently proposed non-invasive measurement method that quantitatively reflects the function of the glymphatic system. DTI-ALPS has been validated in a previous human-based study,which demonstrated a significant correlation between the DTI-ALPS index and the glymphatic function calculated by classical intrathecal injection of contrast agent, indicating that the DTI-ALPS index can reflect the clearing function of the glymphatic system.\u0026nbsp;\u003csup\u003e[19]\u003c/sup\u003e\u0026nbsp; \u0026nbsp;Additionally, the measurement of DTI-ALPS is simple and can yield results within a few minutes, allowing for the real-time reflection of glymphatic system function.\u0026nbsp;\u003csup\u003e[32]\u003c/sup\u003eDTI-ALPS exhibits good stability, which has been confirmed in previous studies, and our research has also demonstrated good inter-observer consistency. Therefore, DTI-ALPS has been applied in the detection and monitoring of many clinical conditions including Alzheimer\u0026rsquo;s disease, hydrocephalus, diabetes,Parkinson\u0026rsquo;s disease and has shown a correlation with the severity of clinical diseases.\u003csup\u003e[33-36]\u003c/sup\u003e To the best of our knowledge, DTI-ALPS has not yet been applied to the detection of pPD, especially for longitudinal follow-up to predict the conversion of pPD to PD.\u003c/p\u003e\n\u003cp\u003eWe have observed that the DTI-ALPS index is significantly lower in the pPD group compared to HC. We know that pPD is the preclinical stage of PD, which usually lasts for decades and is often not recognized early due to its weak and subtle clinical symptoms. However, previous studies have shown that pathological changes occur long before the appearance of typical PD motor symptoms.\u0026nbsp;\u003csup\u003e[37]\u003c/sup\u003eWe speculated that the glymphatic system dysfunction had already occurred in PPD stage. A previous study on iRBD population also obtained similar results, showing that the DTI-ALPS index in the iRBD group was significantly lower than that in the HC group.\u003csup\u003e[23]\u003c/sup\u003e iRBD is a representative group of the general pPD population, and our PPD population contains 61.6% of iRBD patients, but our cohort may be more representative of the general pPD population. However, we also noted that there was no significant difference in DTI-ALPS index between the pPD and the PD group, which we analyzed may be due to the fact that the included PD population was mostly early PD patients, and compensatory mechanisms (such as AQP4 redistribution) may temporarily stabilize lymphatic clearance and mask further decline.\u003csup\u003e[38]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur results showed that glymphatic function was negatively correlated with STAI score in the pPD group, after controlling for age, sex, and education. Previous surveys of non-motor symptoms in people with pPD have shown that anxiety is a very common neuropsychiatric symptom of pPD.\u003csup\u003e[39]\u003c/sup\u003eAnother animal study on mice after sleep deprivation showed that mice with chronic sleep restriction had reduced glymphatic function and showed significant anxiety-like behavior, It suggests that dysfunction of The glymphatic system may play a role in the development of anxiety-like behaviors of mice after chronic sleep restriction.\u003csup\u003e[40]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, with regard to the CSF biomarkers, glymphatic function was positively correlated with p-Tau and t-Tau level in pPD ,which highlights a critical interplay between protein clearance mechanisms and neurodegenerative pathology. Tau, a microtubule-associated protein, is physiologically released into the extracellular space and subsequently cleared via the glymphatic system\u0026mdash;a process dependent on astrocytic aquaporin-4 (AQP4) water channels that facilitate cerebrospinal-interstitial fluid exchange.\u0026nbsp;\u003csup\u003e[41]\u003c/sup\u003e In PD, while \u0026alpha;-synuclein aggregation is the primary hallmark, impaired glymphatic function disrupts the clearance of multiple neurotoxic proteins, including tau. Animal studies demonstrate that AQP4 deficiency reduces tau efflux from brain parenchyma to CSF, leading to its pathological accumulation.\u0026nbsp;\u003csup\u003e[42]\u003c/sup\u003e Our findings align with this mechanism, showing that glymphatic dysfunction emerges as early as the pPD stage, preceding clinical PD onset. This dysfunction compromises tau clearance, potentially exacerbating \u0026alpha;-synuclein aggregation through synergistic neurotoxicity or neuroinflammatory cascades.\u003c/p\u003e\n\u003cp\u003eThe observed correlations between reduced DTI-ALPS index and GMV alterations in the left postcentral gyrus, right superior occipital gyrus, right temporal pole, and left thalamus suggested a potential interplay between glymphatic dysfunction and region-specific neurodegeneration. The glymphatic system, regulated by AQP4 polarization and perivascular fluid dynamics, is critical for clearing neurotoxic proteins such as \u0026alpha;-synuclein and amyloid-\u0026beta;, which accumulate in PD pathogenesis.\u003csup\u003e[43]\u003c/sup\u003e Impaired glymphatic activity, reflected by diminished DTI-ALPS index, may exacerbate protein aggregation, neuroinflammation, and subsequent GMV loss in metabolically vulnerable regions. The left thalamus, a hub for sensorimotor integration, exhibits high metabolic demand and is susceptible to impaired waste clearance, as evidenced by thalamic atrophy in neurodegenerative disorders linked to glymphatic failure.\u0026nbsp;\u003csup\u003e[44]\u003c/sup\u003eSimilarly, the right temporal pole, implicated in social-emotional processing, is an early site of \u0026alpha;-synuclein pathology in PD, where glymphatic dysfunction may accelerate synaptic loss and structural decline.\u0026nbsp;\u003csup\u003e[12]\u003c/sup\u003eThe association with the left postcentral gyrus and right superior occipital gyrus highlights spatial specificity in glymphatic impairment. The postcentral gyrus, integral to somatosensory processing, may experience microstructural damage due to disrupted perivascular fluid exchange, akin to mechanisms observed in cerebral small vessel disease.\u003csup\u003e[45]\u003c/sup\u003e The superior occipital gyrus, involved in visual processing, could similarly undergo atrophy from impaired metabolite clearance, as glymphatic failure disrupts fluid homeostasis in regions with high interstitial solute production.\u0026nbsp;\u003csup\u003e[46]\u003c/sup\u003eFuture studies should validate these findings in longitudinal cohorts and explore therapeutic strategies targeting AQP4-mediated fluid transport to mitigate region-specific neurodegeneration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA recent longitudinal study of PD found that DTI-ALPS index was associated with PD symptom severity and significantly associated with faster PD disease progression,which suggested that DTI-ALPS index might be a biomarker for predicting the progression of PD disease.\u0026nbsp;\u003csup\u003e[47]\u003c/sup\u003eIn this study, we analyzed 4-year longitudinal follow-up data of PD in prodrome. In a multi-factor COX regression model, the baseline DTI-ALPS index level was an independent predictor from pPD conversion to PD. This finding is consistent with the results of previous studies and further expands the value of DTI-ALPS index in the diagnosis and prediction of PD. We can speculate that in the prodromal PD stage, glymphatic dysfunction has emerged, leading to the clearance of neurotoxic proteins (such as \u0026alpha;-syn), which in turn causes abnormal aggregation of \u0026alpha;-syn and promotes the progression of the disease to PD. DTI-ALPS index, as a non-invasive neuroimaging index, can help us identify high-risk patients in advance and provide an important basis for early diagnosis and treatment of PD. This provides important clues for the development of new therapeutic targets and the selection of better treatment timing.\u003c/p\u003e\n\u003cp\u003eOur study had several limitations.First, the relationship between\u0026nbsp;DTI-ALPS and human glymphatic function remains to be thoroughly and rigorously confirmed by pathophysiological studies. The DTI-ALPS , in addition to reflecting the diffusion rate along the perivascular space, may incorporate the contribution of vascular pulsation, tissue fluid dynamics, and tissue microstructure. Therefore, caution should be exercised when interpreting the association between the Alpine index and glymphatic clearance.\u0026nbsp;\u003csup\u003e[48]\u003c/sup\u003eSecond, it is currently unclear whether these pPD participants will ultimately develop PD as a result of short-term follow-up. This is a trap that has existed in all studies to date. Third, the follow-up analysis of pPD subjects had a small sample size and a relatively short follow-up time, and future studies will need a larger sample size and longer follow-up time.Fourth, the pPD subjects in this study were \u0026nbsp;pPD population defined by PPMI, which was slightly different from International Parkinson\u0026apos;s Disease and Movement Disorders Association (MDS) research criteria for pPD\u0026nbsp;(MDS-pPD)\u003csup\u003e[49]\u003c/sup\u003e,so the results need to be verified by the subjects who meet the MDS--pPD criteria in the future.Fifth, it is recommended that future studies include metabolic risk factors such as hypertension and diabetes.\u003c/p\u003e\n\u003cp\u003eIn summary, assessing brain glymphatic dysfunction in individuals with pPD using DTI-ALPS revealed glymphatic alterations. In individuals who phenotypically convert to PD, the reduction of glymphatic activity was more severe. Therefore, DTI-ALPS may contribute in identifying individuals with pPD at a high risk for conversion to PD, thus enabling earlier intervention and potentially more effective disease management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipant\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed data from participants with pPD, dnPD patients and healthy controls,de novo , all of whom were part of the Parkinson's Disease Progression Markers Initiative (PPMI). PPMI is a prospective, longitudinal, observational multicenter study aimed at validating biomarkers of PD progression.\u003c/p\u003e\n\u003cp\u003epPD patients were identified as individuals at risk for developing PD based on clinical characteristics, genetic variations, or other biomarkers, such as RBD, genetic risk variants (LRRK2, GBA), the University of Pennsylvania Smell Identification Test (UPSIT)-based hyposmia, and positive dopamine transporter (DAT) SPECT on visual inspection. Detailed criteria for participant inclusion are available online at ppmi-info.org/study-design. dnPD patients were drug-naive,\u0026nbsp;newly diagnosed PD patients.\u003c/p\u003e\n\u003cp\u003eStudy visits were conducted at baseline, followed by assessments at 3-month intervals during the first year and biannually thereafter. \u0026nbsp;To minimize confounding effects of pharmacotherapy, the pPD cohort strictly excluded individuals receiving PD-specific medications, consistent with the PPMI protocol. \u0026nbsp;Data for this analysis were retrieved from the PPMI database in May 2023.Participant inclusion criteria were as follows:1. All participants were required to have high-quality 3D T1-weighted magnetic resonance imaging (MRI) and DTI datasets acquired at baseline. 2. To ensure homogeneity in imaging protocols, only data obtained from 3.0 Tesla Siemens MRI scanners (MAGNETOM Trio/TIM/Skyra/Vida systems) using harmonized acquisition parameters (e.g., repetition time, echo time, voxel resolution) were included. 3. The pPD cohort was restricted to individuals with a minimum follow-up duration of 4 years to enable robust assessment of conversion patterns. 4. Participants were excluded if MRI scans exhibited motion artifacts, field inhomogeneities, or acquisition errors that precluded quantitative analysis.\u003c/p\u003e\n\u003cp\u003ePD converters were operationally defined as individuals meeting the Movement Disorder Society (MDS) 2015 Clinical Diagnostic Criteria for Parkinson's Disease\u003csup\u003e[28]\u003c/sup\u003e on two consecutive clinical evaluations. \u0026nbsp; To enhance diagnostic specificity, two participants initially classified as PD converters were later excluded after re-evaluation confirmed alternative α-synucleinopathies (dementia with Lewy bodies and multiple system atrophy).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPMI study is registered at Clinical Trials.gov (NCT01141023). Each participating PPMI site received approval from an ethical standards committee on human experimentation before the start of the study. Written informed consent for the study was obtained from all participating individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent comprehensive clinical assessments at baseline using standardized scales to evaluate motor and non-motor symptoms. Motor symptoms were assessed with the Unified Parkinson’s Disease Rating Scale, Part III (UPDRS III). Non-motor assessments included: UPSIT ( 40-item odor identification, scores ≤ 35 suggesting hyposmia), RBDSQ (scores ≥6 indicating risk), the Epworth Sleepiness Scale (ESS; daytime sleepiness severity, ≥10 indicating excessive sleepiness), the Montreal Cognitive Assessment (MoCA; cognitive screening, scores ≤26 indicating impairment), the State-Trait Anxiety Inventory (STAI; 20-item state and 20-item trait anxiety subscales, higher scores reflecting greater anxiety), and the 15-item Geriatric Depression Scale (GDS-15; scores ≥5 suggesting depression). Autonomic dysfunction was evaluated using the Scale for Outcomes in Parkinson’s Disease-Autonomic (SCOPA-AUT; scores 0–69, higher scores indicating worse dysfunction). Further details on these scales, including validation and administration, are described in prior work.\u003csup\u003e[29, 30]\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage Acquisition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-contrast enhanced 3D volumetric T1-weighted MRI and DTI scans of the total subjects were acquired on 3T Siemens MRI scanners (Erlangen, Germany) using an MPRAGE sequence at different centers. Acquisition parameters and detailed protocols are available on the PPMI website. The following indexes were included in the protocol: (1) DTI: 72 axial slices, echo time (TE) = 88 ms, repetition time (TR) = 500–9,000 ms, voxel size: 2.0 × 2.0 × 2.0 mm3 , acquisition matrix = 1,044 × 1,044; one diffusion-unweighted (b0) image and 64 diffusion-sensitive gradient directions at b = 1,000s/mm2 images; (2) 3D-T1WI: TE = 2.98 ms, TR = 2,300 ms, voxel size: 1.0×1.0×1.0 mm3 , acquisition matrix = 240 × 256. Staff at each center were trained to ensure that the data were collected in a standardized manner. In subsequent data analysis, subjects with missing data were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDTI-ALPS index Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe adopted the method for DTI-ALPS processing and measurement from the previous publication.\u003csup\u003e[18]\u003c/sup\u003eFirst, the diffusion weighted images were processed by FMRIB software (FSL, FSL/http://www.fmrib.ox.ac.uk/). The process includes the following steps: (1) We converted DTI images in raw data into 4D NIfTI format files using dcm2nii software. The head motion and eddy current of DTI image were corrected. (3)The skull of the scalp was removed to obtain brain tissue. (4)The dtifit command of FSL was used \u0026nbsp;to reconstruct the tensor from the DTI image, the color fraction anisotropy (FA) image was obtained.\u003c/p\u003e\n\u003cp\u003eAt the level of the lateral ventricle, the direction of the subcortical fibers and the perivascular space is almost the same, both perpendicular to the lateral ventricle, mainly in the left and right direction, that is, the X-axis.The associated fibers are mainly in the forward and backward direction (y axis), while the projection fibers maintain the up and down direction (z axis). The directions of perivascular space, associated fibers and projective fibers are perpendicular to each other.\u003c/p\u003e\n\u003cp\u003eTwo trained neuroradiologists evaluated all images without knowledge of clinical data, independently selecting region of interest (ROI). A spherical ROI with a diameter of 4mm was placed on projective fibers, associated fibers, and subcortical fibers in the left and right hemispheres. Fsleyes software was used to extract x, y, z direction fusion fibers Dxx, Dyy and Dzz for projection and association fibers within ROI. The DTI-ALPS index for the left and right hemispheres was calculated separately, and then the average DTI-ALPS index for both sides was used for the main analysis. (Fig.5)\u003c/p\u003e\n\u003cp\u003eThe formula for calculating DTI-ALPS index is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"236\" height=\"30\" src=\"data:image/png;base64,R0lGODlh7AAeAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAwDsABsAhgAAAAAAAAAAOgAAZgAA/wA6OgA6ZgA6kABmkABmtgFmtgD//zoAADoAOjoAZjo6ADo6Zjo6kDpmkDpmtjqQtjqQ22YAAGYAOmYAZmY6AGY6kGZmOmaQkGaQtmaQ22a2tma222a2/5A6AJA6OpA6ZpBmOpBmZpBmkJC225Db25Db/7ZmALdmOrZmOrZmZraQOraQZraQtrbbtrbb27bb/7b/trb//9uQOtuROtuQZtu3Ztu2Ztu2kNvbkNv/ttv/29v///+2Zv+3Zv+2kP+2tv/bkf/bkP/btv//tv//2////wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwf/gACCg4SFhoeIiYqLjI2Oj4xHJjSQlZaXmIY6Hpmdnp+gl0gaQABBAagBEqWhhUkiIYSnqautlUUTtrq7vI83sYI3CQBHIgesvYXCxMbIyYgsKs/Tu6ccGQIfJQHHADsMAbkARhnhQNblFYRJJDaDywBIFyrfCUcZCfX3CTcBHODq0B37FsCAtH7A3g2LN08fPofDyJlLsi1AAoqoFhI0KIjgAHcAcKijRjLUCnU3jiHBYMOIAxqvQhiJAMSlu5OmuglaCRIAPCQWYgXp8GJCqaFFgbxSZxPAyVE9Gkh7cSymsoVAhRI1amprTZo2jUBoOUEs2XFSAVCtKZWnICEK/0rK9bRC6DCes1BVSNLiQYCPToXqjMdS4c55glYMVlzKqjxpdQUF6Rbko1VCPxE7XdyNr1/AORhAoBR6dM6jHyc7AwB3riWgARJOixzkLkuzAHjQSFnTwU3ByNj1XJaEMQAiMxRTQq78FaemkdEerArL1ELixpkfWJ58O2+bQVAmCO8zYtrvL5PEYCVSmQBpOy0EeE8IdqoCKARhnN9hNW9HWZF0ygA1yBfCChb5pNc4DAjAgQXj/VVgbJgBk5cAteQg1TcHwLAhAwf8IAIqAoBgynzwhVYQJVbVdiKJGX7YoYw9NPhghPOFMIsAwKhoACUAtJDjINGsM+JIghiRVv8hSkpTHH0rIFDKDYBhNtgiAbpmyyirQXKZI0mMII0LPWl5CC6FBDGBcUkuSUiTSTJQQZZuZZKlmaEccQKQlgxYpiJJwMAJnopsYkhdQdDXJnxvLmkEAyG8cqUsf9kwoAzlGAASX6gUAKl+QqpSCoKxPRoAkq+koio3XRLq6qvj0AQUknAymVYxgJEjAFeGVOZOohTUJGdi23lD4WZKibBQC5oGKRus0EaryA0jsVnrm+CgYpp+MDT4rCmA+QrAUvEEFV8sWapGbAnfQrLqu/DGK++89NZr77345ltvuaoqeu0g/7qyQpWDiCsuubUGaGoqVSKBjyGpwjuptBRT42L/fCPVGqY0Ac/ESqKMShYuYAgvqbCbhLTwgQVIVuxytOTq14x0ggzhG4PPPmpicZMaTLII6rwiZRIvDCtpKTzkwhe6LL/sNKzyHUvqAT5EzbAN9skWaLYR8BknNzWySipTmc4wIrrlTDTiMf2c+vTbcMfttCRey82LoXbnjQiXL6JSSy9f5iUqnmjqbfg7CS1TzMTJKD6zmUUe3gs62WzTDUHiSGQUOm4PIpxhDKUwYgL2WQSUABv4A9CJ11wODkcIYYbVPBeqUPs45Ri130UVafQ6fB6B1J7kveDEG08uwQSLx9AFNFid5Z17BGI5VDC9NNUjfPNTpMC51pfRl6tj/KQjmBgE+SYy75tZRpQ1Vvs0r9Vkna0Rzwtttlmq6l59VRrYaYOAXmYOMidS+KSAyUIXYqIDQF+Bb4DjKp/nJDiu/oFGNKTBYANTM6n62U8X+CNMS8aSm90cAzrA8Vw7DMOzUshjA8B4YSycM47tJQROvGnRMLBTFQm2QAVhMtEP0WMp8ZBnGTg8YXrWI4jhfbAVA5rQgRLUNqbYCEJRNFCFJJMKDCHjP8HozIjmYz4UDcJHLKpObS5Ui7ahyI3veZSDsEgiHdVREGgUhJB4RKSQPfFpfEvExiYIH/A1YpBkklbh/hg3PdVtECsoESQlyUWCMSJQg6IY3gAQCAA7\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eDxxproj: diffusivity along the x-axis in the projection fiber, Dxxassoci: diffusivity along the x-axis in the association fiber, Dyyproj: diffusivity along the y-axis in the projection fiber, Dzzassoci: diffusivity along the z-axis in the association fiber.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGray matter volume measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline brain MRI sequences were derived from the PPMI database.We applied a voxel-based morphometry method using high-resolution 3-D T1-weighted images. By affine and high-dimensional nonlinear registration, the individual image was spatially normalized to the standard stereotactic space,by using CAT12 toolbox \u0026nbsp;(dbm.neuro.uni-jena.de/cat/) and MATLAB SPM12 (fil.ion.ucl.ac.uk/spm/software/ spm12/). We segmented MRI images into gray matter and white matter in the standard space of the Montreal Neurological Institute (MNI) in high dimension, and then segmented gray matter into different brain regions using the AAL3 template.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed by SPSS 26.0 and R statistical software (version 3.4.3; http://www.r-project.org/) . Statistical plots were generated using GraphPad Prism 8.0a (GraphPad Inc., San Diego, CA, USA).\u003c/p\u003e\n\u003cp\u003eNormally distributed Continuous variables (assessed via Shapiro-Wilk test)were represented by mean ± standard deviation (SD). These included age, years of education, MoCA ,ESS scores and cerebrospinal fluid concentration of\u0026nbsp;α-syn, t-tau, p-tau . Comparisons between groups were performed using one-way ANOVA . Non-normally distributed variables (skewed data) were represented by median (1st quartile, 3rd quartile). These included UPSIT, RBDSQ, SCOPA-AUT, STAI, UPDRS III and GDS-15 scores. Comparisons between groups were performed using the Kruskal-Wallis test. Categorical Variables reported as percentage. These included sex, GBA status, and LRRK status. Comparisons between groups were performed using the chi-square test or Fisher’s exact test (for cells with expected counts \u0026lt;5). Bonferroni correction was applied to adjust for multiple comparisons. \u0026nbsp;Missing values (\u0026lt;5% of total data) were imputed using a K-Nearest Neighbor (KNN) weighted imputation method (k=10 neighbors, Euclidean distance metric).A two-tailed p-value \u0026lt;0.05 was considered statistically significant. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn pPD group we performed a partial Pearson/Spearman correlation analysis with age, sex, and education as covariates(Education was included as a covariate due to its association with cognitive reserve, which may influence neurodegenerative progression\u003csup\u003e[31]\u003c/sup\u003e) to identify the relationship between DTI-ALPS index and gray matter volume, as well as the relationship between DTI-ALPS index and various clinical features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline variables with potential prognostic relevance \u0026nbsp;were first screened using univariate Cox proportional hazards models. Variables with a significance level of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1 were retained for further analysis to avoid premature exclusion of weakly associated predictors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address overfitting and select the most parsimonious set of predictors, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was applied to the retained variables. The optimal tuning parameter (lambda\u0026nbsp;λ) was determined by minimizing the partial likelihood deviance.\u003c/p\u003e\n\u003cp\u003eVariables selected by LASSO were subsequently incorporated into a multivariable Cox proportional hazards model to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe influence of \u0026nbsp;DTI-ALPS index levels on pPD to PD conversion was examined by Kaplan-Meier survival analysis,with differences between groups evaluated through the log-rank test.Double-tail test was used, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eSensitivity analysis was performed by multi-model cox regression analysis to examine the robustness of our results.Model 1 is crude. Model 2 includes three confounding factors:sex, age, education（Education was included as a covariate due to its association with cognitive reserve, which may influence neurodegenerative progression).\u003csup\u003e[31]\u003c/sup\u003e Model 3 was adjusted for sex, age, education, RBDSQ, STAI_anxiety_total scores. Model 4 was further adjusted for sex, age, education, RBDSQ, STAI_anxiety_total scores,GBA_PATHVAR, LRRK2_PATHVAR. Hazard ratios (HRs) with 95% confidence intervals (Cis) were estimated to determine the risk of PD conversion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial Disclosure:\u0026nbsp;\u003c/strong\u003eThere is no Conflict of Interest concerning the research related to the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData used in the preparation of this study were obtained from the Parkinson\u0026rsquo;s Progression Markers Initiative database. All data are full access and available at www.ppmi-info.org.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Guangzhou Science and Technology Project(2024A03J1109) and research project of Chinese Medicine Bureau of Guangdong Province(20251277). \u0026nbsp;The authors gratefully thank all study participants for their participation in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW : conceived and designed the experiments. ZK and JH: contributed signifcantly to the experiments. JW,YZ:arranging data and performing data analyses. AC: wrote the draft manuscript. PK and YC, revised the manuscript. All authors read and approved the fnal manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors report no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson\u0026apos;s disease. Lancet Neurol. 2021. 20(5): 385-397.\u003c/li\u003e\n\u003cli\u003eBloem BR, Okun MS, Klein C. Parkinson\u0026apos;s disease. Lancet. 2021. 397(10291): 2284-2303.\u003c/li\u003e\n\u003cli\u003eCosta HN, Esteves AR, Empadinhas N, Cardoso SM. Parkinson\u0026apos;s Disease: A Multisystem Disorder. Neurosci Bull. 2023. 39(1): 113-124.\u003c/li\u003e\n\u003cli\u003eKoeglsperger T, Rumpf SL, Schlie\u0026szlig;er P, et al. Neuropathology of incidental Lewy body \u0026amp; prodromal Parkinson\u0026apos;s disease. Mol Neurodegener. 2023. 18(1): 32.\u003c/li\u003e\n\u003cli\u003ePostuma RB, Berg D. 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Neuroradiology. 2024. 66(2): 157-160.\u003c/li\u003e\n\u003cli\u003eBerg D, Postuma RB, Adler CH, et al. MDS research criteria for prodromal Parkinson\u0026apos;s disease. Mov Disord. 2015. 30(12): 1600-11.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1-4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5827541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5827541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated glymphatic system integrity in prodromal Parkinson\u0026rsquo;s disease (pPD) using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) technique, analyzing data from 51 healthy controls, 83 pPD individuals, and 202 de novo Parkinson\u0026rsquo;s disease (dnPD) patients. The DTI-ALPS index was significantly reduced in both pPD and dnPD groups compared to controls and correlated with anxiety scores and cerebrospinal fluid tau levels in the pPD group. Longitudinal analysis revealed that a higher DTI-ALPS index was associated with a lower risk of conversion from pPD to clinical PD, suggesting that glymphatic dysfunction in pPD may serve as a predictive biomarker for disease progression.\u003c/p\u003e","manuscriptTitle":"Neuroimaging-Based Glymphatic Function Predicts Conversion from Prodromal to Manifest Parkinson's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 09:47:08","doi":"10.21203/rs.3.rs-5827541/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdaa7124-3198-44f8-b282-1727109f386e","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47807689,"name":"Biological sciences/Neuroscience"},{"id":47807690,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-06-15T05:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 09:47:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5827541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5827541","identity":"rs-5827541","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}