The definition of Parkinson’s disease (PD) is undergoing a profound transformation from a “clinical syndrome” to a “biological entity”, with development of two objective biological classification systems, the NSD-ISS (Neuronal Alpha-Synuclein Disease Integrated Staging System) and the SynNeurGe framework. Multimodal diagnostic tools further improve the detection of PD. This review synthesizes advances in three key domains of PD detection. First, fluid and tissue biomarkers, particularly using α-synuclein (αSyn) seed amplification assays, allow detection of synucleinopathy in cerebrospinal fluid, blood, saliva, and skin. This supports pathological diagnosis and differential classification. Extracellular vesicles provide cell-type-specific cargo profiles, while neurofilament light chain indicates neuroaxonal injury. Second, neuroimaging captures in vivo pathology. MRI identifies nigral degeneration (nigrosome-1 loss, iron accumulation, neuromelanin depletion), MRS reveals metabolic and neurotransmitter imbalances, and αSyn positron emission tomography tracers enable direct visualization of aggregation. Third, digital biomarkers derived from wearable devices, videos, and audios quantify real-world motor and non-motor symptoms, enabling continuous, ecological monitoring. Integration of these complementary biomarker streams is essential for biological definition and stratification of PD patients, and development of targeted therapies. Standardization of assays, multicenter validations, and clear guidance on who should be tested, when testing is appropriate, and how results should inform diagnosis, stratification, or monitoring are required for the translation of these methods into clinical practice in PD.

Parkinson’s disease (PD) involves widespread brain network dysfunction, yet the neural mechanisms underlying the highly heterogeneous clinical response to dopaminergic therapy remain poorly understood. To address this gap, we applied whole-brain Co-activation Pattern (CAP) analysis to resting-state fMRI data from healthy controls and a longitudinal cohort of PD patients evaluated in both OFF- and ON-medication states. Our findings suggest that PD is characterized by a pathologically shifted dynamic state repertoire. Crucially, we identified a specific cortico–basal ganglia–cerebellar integrative state (CAP-2) that may represent a treatment-sensitive network configuration. Following levodopa administration, clinical responders adaptively maintain this network configuration. Conversely, non-responders exhibit a marked depletion of CAP-2 dynamics, consistent with a failure of adaptive network reconfiguration, characterized by the marked reduction of CAP-2 occurrences (counts) and abnormally rigid state transition dynamics. Furthermore, integrating these CAP-2 transition features into classification models provided proof-of-concept evidence that this cortico–basal ganglia–cerebellar network configuration captures mechanistic information underlying treatment heterogeneity. These findings underscore that dopaminergic response depends on adaptive macroscopic reconfiguration, positioning the dynamic depletion of CAP-2 as a proof-of-concept exploratory target for understanding treatment heterogeneity in PD.

The basal ganglia are characterized by somatotopic representation and are organized in parallel, functionally segregated corticostriatal circuits, but the impact of Parkinson's disease (PD) on this architecture is unknown. We mapped task-evoked dopamine release using [11C]raclopride positron emission tomography/magnetic resonance in 13 early PD and 15 healthy control (HC) subjects during the performance of motor, cognitive, and reward tasks. In PD, motor tasks elicited decreased relative dopamine release in dorsal putamen but increased in less affected caudate and ventral striatum compared to HC. HC showed distinct dopaminergic topographies for upper versus lower limb activation; this somatotopy was absent in PD. HC exhibited segregated patterns of dopamine release across all tasks, but both anatomical and functional segregation were lost in PD. Compensatory effects of altered dopamine release topography may help to maintain function in patients with PD, but loss of segregation may contribute to challenges in discrete selection of movement and in multitasking.

Background: China will host the world’s largest population of the oldest-old (aged ≥80 years) by 2050. We benchmarked the major yet under-quantified neurological burden of China's oldest-old against G20 peers to inform targeted healthy longevity strategies. Methods: Using the Global Burden of Disease Study 2023, we analysed prevalence, incidence, mortality, and disability-adjusted life-years (DALYs) for 23 neurological disorders in Chinese adults aged ≥80 years from 1990 to 2023. We benchmarked China against G20 peers, assessed temporal trends using average annual percentage change (AAPC) and percentage change, decomposed drivers of prevalence change, and projected trends to 2050 using a Bayesian Model Averaging framework. Findings: In 2023, neurological disorders among China's oldest-old accounted for 26·8 million (95% uncertainty interval 21·4 to 33·3) cases and 1·39 million (0·86 to 2·25) deaths, representing 33·3% and 41·1% of G20 totals, respectively. Although age-standardised mortality declined (AAPC: -2·01% [95% confidence interval -2·10 to -1·92]), age-standardised prevalence increased (0·38% [0·37 to 0·38]), marking the fastest growth among the G20 from 1990 to 2023. China’s oldest-old exhibited a 3·6-fold higher age-standardised DALY rate (ASDR) than the younger elderly (60–79 years). Dementia surpassed stroke as the leading cause of DALYs among China's oldest-old since 2019, although ischaemic stroke remained a major cause of death. Epidemiological changes contributed to increases in dementia (81·8%) and ischaemic stroke cases (88·0%) among China’s oldest-old from 1990 to 2023. Projections suggest dementia will solidify dominance in ASDR by 2050; however, optimised control of metabolic and behavioural risk factors could reduce 2050 dementia and stroke subtype ASDRs by 14·9% and 39·3–49·2% among China’s oldest-old, respectively. Interpretation: China’s oldest-old face a survival-disability paradox, where declining mortality has expanded the population with chronic neurological disability, particularly dementia. This transition necessitates a strategic shift from acute, hospital-centric care to integrated long-term care systems and comprehensive life-course prevention.

Dorsal nigral hyperintensity (DNH) abnormality associated with excessive iron deposition in the substantia nigra, is recognized as an imaging characteristic of Parkinson’s disease (PD) and can be effectively visualized using 7T MRI. This study was aimed to develop and validate the optimal DNH assessment method as a biomarker for PD, idiopathic rapid eye movement sleep behavior disorder (iRBD), and Parkinson-plus syndromes, and to explore the nigral iron deposition patterns in these diseases. Three-dimensional gradient-echo T2*-weighted images were acquired by 7T MRI from a total of 402 patients and 100 healthy controls (HCs) in two independent cohorts (development and validation cohorts). Seven methods, including four dichotomous methods and three DNH rating scales, were used to assess DNH and evaluate their diagnostic performance. R2* mapping and principal component analysis were performed to assess nigral iron deposition patterns. Bilateral DNH detection rates in the development cohort were 22.6

Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, highlighting the urgent need for improved diagnostic and therapeutic strategies. Biomarkers from cerebrospinal fluid (CSF), blood, and peripheral tissue hold promise for early PD detection. In addition, neuroimaging techniques, including magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET), allow for detailed visualization of neurodegeneration and associated structural and functional brain changes. This review summarizes recent advances in PD biomarkers and neuroimaging, highlighting their diagnostic potential and implications for future research.

Voxel-level detection of task-induced striatal dopamine (DA) release in humans is achievable with dynamic PET imaging, enabling complex studies of motor, cognitive, and reward tasks. We previously introduced a data-driven methodology termed Residual Space Detection (RSD), which improved detection of low-amplitude DA release, however its applicability was limited to detection of low-amplitude and/or localized effects. Here, we generalize RSD to broader DA release scenarios by introducing a novel model-based baseline time-activity curve prediction method in combination with non-local-means clustering (RSD-Hybrid-IMRTM). In simulations, RSD-Hybrid-IMRTM outperforms our previous methodology for detecting global striatal DA release, improving absolute detection sensitivity by 18% at 5% false positive rate, while also demonstrating the ability to track the magnitude of task-induced changes in synaptic DA concentrations in a noise-robust manner. As a proof of principle, we apply RSD-Hybrid-IMRTM to healthy controls and Parkinson's disease subjects undergoing finger and foot tapping tasks. Results reveal expected group differences in parametric maps, parameter magnitudes, and functional segregation, demonstrating RSD-Hybrid-IMRTM's utility for investigating neurotransmission in human cohorts.

BACKGROUND:Freezing of gait (FOG) is a common gait disorder that often accompanies Parkinson's disease (PD). The current understanding of brain functional organization in FOG was built on the assumption that the functional connectivity (FC) of networks is static, but FC changes dynamically over time. We aimed to characterize the dynamic functional connectivity (DFC) in patients with FOG based on high temporal-resolution functional MRI (fMRI). METHODS:Eighty-seven PD patients, including 29 with FOG and 58 without FOG, and 32 healthy controls underwent resting-state fMRI. Spatial independent component analysis and a sliding-window approach were used to estimate DFC. RESULTS:Four patterns of structured FC 'states' were identified: a frequent and sparsely connected network (State I), a less frequent but highly synchronized network (State IV), and two states with opposite connecting directions between the visual network and the sensorimotor network (positively connected in State II, negatively connected in State III). Compared with the non-FOG group, patients with FOG spent significantly less time in State II and more time in State III. The longer dwell time in State III was correlated with more severe FOG symptoms. The fractional window of State III tended to correlate to visual-spatial and executive dysfunction in FOG. Moreover, fewer transitions between brain states and lower variability in local efficiency were observed in FOG, suggesting a relatively 'rigid' brain. CONCLUSIONS:This study highlights how visuomotor network dynamics are related to the presence and severity of FOG in PD patients, which provides new insights into understanding the pathophysiological mechanisms that underly FOG. © 2025 International Parkinson and Movement Disorder Society.

Aim: Wilson's disease (WD) primarily manifests in hepatic and neurological symptoms. This study aims to integrate multimodal neuroimaging and hepatic imaging analysis to provide novel insights into the diagnosis and severity assessment of WD. Methods: This study recruited patients diagnosed with hepatic-type WD (HWD), neurological-type WD (NWD), and healthy controls (HCs). All participants underwent both brain and liver magnetic resonance imaging (MRI) scanning. The quantitative susceptibility mapping (QSM) values, volumes of different brain regions, fat content, and iron quantification in liver regions of interest (ROIs) were compared and analyzed across groups. The diagnostic biomarkers were identified by LASSO (Least Absolute Shrinkage and Selection Operator) regression, and their diagnostic value was evaluated using receiver operating characteristic (ROC) analysis. In addition, the correlation between the severity of neurological symptoms and imaging biomarkers was analyzed. Results: A total of 38 subjects were included in this study, comprising 17 cases of HWD, 10 NWD, and 11 HCs. Compared to HCs, the WD group, especially NWD, exhibited significant iron deposition in liver segments. Additionally, QSM values were significantly increased. The regional brain volumes were significantly reduced. The ROC curve demonstrates that the combination of brain QSM values and volumes selected exhibits strong discriminatory power in distinguishing between WD vs. HCs, NWD vs. HCs, HWD vs. HCs, and NWD vs. HWD. Conclusion: Iron deposition in the liver and brain, as well as the extent of regional brain atrophy, may serve as predictive markers for the onset of WD, particularly NWD. All suspected and confirmed WD patients, regardless of NWD or HWD, should undergo brain and liver MRI for diagnostic evaluation and follow-up assessments.

BACKGROUND:Dyskinesia is a motor complication of Parkinson's disease (PD) posing therapeutic challenges. The optimal therapy for dyskinesia in PD has not been identified due to the lack of comprehensive evaluation of treatments. OBJECTIVE:The aim was to compare the efficacy and safety of interventions for alleviating levodopa-induced dyskinesia in PD. METHODS:We conducted a Bayesian network meta-analysis (NMA) by systematically searching PubMed, Web of Science, Embase, Cochrane Library, ClinicalTrials.gov, and EudraCT databases up to April 1, 2024. The primary efficacy outcome was the change in scores on dyskinesia rating scales from baseline. RESULTS:The study included 85 randomized controlled trials (RCT) involving 13,826 PD patients, comprising 39 interventions. Nine treatments were significantly more effective in reducing scores on dyskinesia rating scales than control (placebo, sham surgery, sham repetitive transcranial magnetic stimulation, or best medical treatment). Globus pallidus interna deep brain stimulation (GPi-DBS) had the highest probability to be the most effective (standardized mean difference, 95% credible interval: -1.27, -1.65 to -0.88; surface under the cumulative ranking curve [SUCRA]: 97.4%), followed by levodopa-carbidopa intestinal gel infusion (SUCRA = 89.7%), subthalamic nucleus (STN)-DBS (SUCRA = 89%), immediate-release (IR) amantadine (SUCRA = 86.5%), pallidotomy (SUCRA = 84.9%), ADS-5102 (SUCRA = 82.9%), clozapine (SUCRA = 77.2%), OS320 (SUCRA = 64.8%), and AFQ056 (SUCRA = 54.5%). GPi-DBS was superior to STN-DBS, and pallidotomy ranked higher than subthalamotomy. ADS-5102 and OS320 had higher adverse event (AE) rates compared to control, whereas AFQ056 and ADS-5102 were linked to more serious AEs. CONCLUSIONS:This RCT-based NMA identifies and ranks nine efficacious interventions for dyskinesia in PD. GPi-DBS may be the most effective therapy for treating dyskinesia, with IR amantadine ranking highest among oral medications. Novel anti-dyskinetic medications are associated with less-favorable tolerance profiles. © 2025 International Parkinson and Movement Disorder Society.

To predict the global, regional, and national prevalence of Parkinson's disease by age, sex, year, and Socio-demographic Index to 2050 and quantify the factors driving changes in Parkinson's disease cases. Modelling study. Global Burden of Disease Study 2021. Prevalent number, all age prevalence and age standardised prevalence of Parkinson's disease in 2050, and average annual percentage change of prevalence from 2021 to 2050; contribution of population ageing, population growth, and changes in prevalence to the growth in Parkinson's disease cases; population attributable fractions for modifiable factors. 25.2 (95% uncertainty interval 21.7 to 30.1) million people were projected to be living with Parkinson's disease worldwide in 2050, representing a 112% (95% uncertainty interval 71% to 152%) increase from 2021. Population ageing (89%) was predicted to be the primary contributor to the growth in cases from 2021 to 2050, followed by population growth (20%) and changes in prevalence (3%). The prevalence of Parkinson's disease was forecasted to be 267 (230 to 320) cases per 100 000 in 2050, indicating a significant increase of 76% (56% to 125%) from 2021, whereas the age standardised prevalence was predicted to be 216 (168 to 281) per 100 000, with an increase of 55% (50% to 60%) from 2021. Countries in the middle fifth of Socio-demographic Index were projected to have the highest percentage increase in the all age prevalence (144%, 87% to 183%) and age standardised prevalence (91%, 82% to 101%) of Parkinson's disease between 2021 and 2050. Among Global Burden of Disease regions, East Asia (10.9 (9.0 to 13.3) million) was projected to have the highest number of Parkinson's disease cases in 2050, with western Sub-Saharan Africa (292%, 266% to 362%) experiencing the most significant increase from 2021. The ≥80 years age group was projected to have the greatest increase in the number of Parkinson's disease cases (196%, 143% to 235%) from 2021 to 2050. The male-to-female ratios of age standardised prevalence of Parkinson's disease were projected to increase from 1.46 in 2021 to 1.64 in 2050 globally. By 2050 Parkinson's disease will have become a greater public health challenge for patients, their families, care givers, communities, and society. The upward trend is expected to be more pronounced among countries with middle Socio-demographic Index, in the Global Burden of Disease East Asia region, and among men. This projection could serve as an aid in promoting health research, informing policy decisions, and allocating resources.

Increasing dopamine levels using oral levodopa administration has been the gold standard for treating Parkinson's disease (PD), but motor complications that occur with the progression of PD seriously affect patient quality of life. Neurorestorative treatments have provided new possibilities for PD therapies. This review summarizes the recent clinical progress in several aspects of neurorestorative strategies: cell therapy, bioengineering and tissue engineering therapy, pharmacological therapy, neurostimulation/neuromodulation, and brain–computer interfaces. However, progress has mainly been related to exploratory experimental results, and more evidence is needed to further verify the safety and efficacy of these neurorestorative treatments in PD.

Existing methods for voxelwise transient dopamine (DA) release detection rely on explicit kinetic modeling of the [11C]raclopride PET time activity curve, which at the voxel level is typically confounded by noise, leading to poor performance for detection of low-amplitude DA release-induced signals. Here we present a novel data-driven, task-informed method-referred to as Residual Space Detection (RSD)-that transforms PET time activity curves to a residual space where DA release-induced perturbations can be isolated and processed. Using simulations, we demonstrate that this method significantly increases detection performance compared to existing kinetic model-based methods for low-magnitude DA release (simulated +100% peak increase in basal DA concentration). In addition, results from nine healthy controls injected with a single bolus of [11C]raclopride performing a finger tapping motor task are shown as proof-of-concept. The ability to detect relatively low magnitudes of dopamine release in the human brain using a single bolus injection, while achieving higher statistical power than previous methods, may additionally enable more complex analyses of neurotransmitter systems. Moreover, RSD is readily generalizable to multiple tasks performed during a single PET scan, further extending the capabilities of task-based single-bolus protocols.

Background:Essential tremor (ET) significantly impacts patients' daily lives and quality of life, presenting a considerable challenge in clinical practice. In recent years, novel therapeutic regimens have been investigated in randomized controlled trials (RCTs). This study aims to investigate and evaluate the relative efficacy and safety of various therapeutic interventions for ET. Methods:We did a systematic review and Bayesian Model-based Network Meta-analysis (NMA) of RCTs. Following PRISMA-NMA guidelines, a comprehensive database search was conducted up to April 1, 2024 to identify RCTs focused on ET treatments. The Bayesian Markov Chain Monte Carlo (MCMC) method was utilized for the analysis, evaluating the relative efficacy and safety of treatments using standardized mean difference (SMD) and log odds ratios (log ORs), respectively. Additionally, the Surface Under the Cumulative Ranking Curve (SUCRA) was applied to assess the relative efficacy of the treatment modalities. PROSPERO registration: CRD42023415752. Findings:This study included 33 RCTs involving 1251 patients, covering 19 oral medication treatments and six non-oral medication treatments. NMA showed that deep brain stimulation (DBS) (SMD = -4.93; 95% CI: [-7.73, -2.13]), CX-8998 (SMD = -2.69; 95% CI: [-5.26, -0.14]), atenolol (SMD = -2.36; 95% CI: [-4.70, -0.10]), and propranolol (SMD = -1.59; 95% CI: [-2.25, -0.67]) showed relative efficacy compared to placebo, with DBS demonstrating relative efficacy compared to 15 other treatment methods. However, GRADE assessment indicated that the evidence level for these conclusions was "low" or "very low." According to SUCRA rankings, DBS (0.97) ranked first in relative efficacy, followed by CX-8998 (0.80), thalamotomy (0.79), atenolol (0.76), metoprolol (0.66), propranolol (0.64), magnetic resonance guided focus ultrasound (MR-FUS) (0.624), ICI-118551 (0.620), nimodipine (0.61) and phenobarbitone (0.59). In terms of safety, as a network graph could not be constructed, DBS and thalamotomy were excluded from the NMA, while other effective treatments showed no significant differences in safety compared to placebo. Interpretation:Our study results indicate that CX-8998, propranolol, and atenolol demonstrate relative efficacy and safety in treating ET. DBS is effective for medication-resistant ET and ranks first in relative efficacy, though our NMA lacks safety data for DBS. Given the low overall grade of evidence, these results should be applied cautiously in clinical practice. Further large-scale, head-to-head RCTs are needed. Funding:This work was supported by grants from the National Nature Science Foundation of China (Grant No. 82271459).

Background: Oral medications and deep brain stimulation (DBS) are first-line treatment strategies for ET management. However, lack of effectiveness and side effects of pharmacological intervention and high cost of DBS are major problems. Non-invasive peripheral vibratory stimulation might be a novel choice for ET management.

BACKGROUND AND OBJECTIVES:Noninvasive and accurate biomarkers of neurologic Wilson disease (NWD), a rare inherited disorder, could reduce diagnostic error or delay. Excessive subcortical metal deposition seen on susceptibility imaging has suggested a characteristic pattern in NWD. With submillimeter spatial resolution and increased contrast, 7T susceptibility-weighted imaging (SWI) may enable better visualization of metal deposition in NWD. In this study, we sought to identify a distinctive metal deposition pattern in NWD using 7T SWI and investigate its diagnostic value and underlying pathophysiologic mechanism. METHODS:Patients with WD, healthy participants with monoallelic ATP7B variant(s) on a single chromosome, and health controls (HCs) were recruited. NWD and non-NWD (nNWD) were defined according to the presence or absence of neurologic symptoms during investigation. Patients with other diseases with comparable clinical or imaging manifestations, including early-onset Parkinson disease (EOPD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and neurodegeneration with brain iron accumulation (NBIA), were additionally recruited and assessed for exploratory comparative analysis. All participants underwent 7T T1, T2, and high-resolution SWI scanning. Quantitative susceptibility mapping and principal component analysis were performed to illustrate metal distribution. RESULTS:We identified a linear signal intensity change consisting of a hyperintense strip at the lateral border of the globus pallidus in patients with NWD. We termed this feature "hyperintense globus pallidus rim sign." This feature was detected in 38 of 41 patients with NWD and was negative in all 31 nNWD patients, 15 patients with EOPD, 30 patients with MSA, 15 patients with PSP, and 12 patients with NBIA; 22 monoallelic ATP7B variant carriers; and 41 HC. Its sensitivity to differentiate between NWD and HC was 92.7%, and specificity was 100%. Severity of the hyperintense globus pallidus rim sign measured by a semiquantitative scale was positively correlated with neurologic severity (ρ = 0.682, 95% CI 0.467-0.821, p < 0.001). Patients with NWD showed increased susceptibility in the lenticular nucleus with high regional weights in the lateral globus pallidus and medial putamen. DISCUSSION:The hyperintense globus pallidus rim sign showed high sensitivity and excellent specificity for diagnosis and differential diagnosis of NWD. It is related to a special metal deposition pattern in the lenticular nucleus in NWD and can be considered as a novel neuroimaging biomarker of NWD. CLASSIFICATION OF EVIDENCE:The study provides Class II evidence that the hyperintense globus pallidus rim sign on 7T SWI MRI can accurately diagnose neurologic WD.

Parkinson's disease (PD) and Multiple System Atrophy (MSA) are both classic neurodegenerative diseases. However, the significant overlap of PD and MSA in clinical symptoms poses a substantial challenge for their diagnosis. To solve this problem, five deep learning-based 3D models are designed to classify PD and MSA using brain Magnetic Resonance Imaging (MRI) of patients. We conduct a comprehensive comparison and evaluation of these models with traditional clinical approaches. The network architecture with the best performance, i.e., ResNet34, is finally determined, which achieves an impressive accuracy of up to 91% and a sensitivity of 99%. In the experiment, we find that the performance of all five DNN methods is superior to that of both traditional clinical analysis methods in the experiment. Our study introduces an innovative method for computer-assisted diagnosis in the early detection of neurological diseases, which has important clinical significance.

BACKGROUND:Parkinson's disease is the second most common neurodegenerative disorder, exhibiting an upward trend in prevalence. We aimed to investigate the prevalence of Parkinson's disease, temporal trends between 1980 and 2023, and variations in prevalence by location, age, sex, survey period, sociodemographic index (SDI), human development index (HDI), and study characteristics (sample size, diagnostic criteria, and data source). METHODS:In this systematic review and meta-analysis we searched PubMed, Cochrane, Web of Science, Embase, Scopus, and Global Health for observational studies that reported Parkinson's disease prevalence in the general population from database inception to Nov 1, 2023. We included studies if they were original observational investigations, had participants from the general population or community-based datasets, and provided numerical data on the prevalence of Parkinson's disease either with 95% CIs or with sufficient information to calculate 95% CIs. Studies were excluded if they were conducted in a specific population, had a sample size smaller than 1000, or were review articles, case reports, protocols, meeting abstracts, letters, comments, short communications, posters, and reports. The publication characteristics (first author and publication year), study location (countries, WHO regions, SDI, and HDI), survey period, study design, diagnostic criteria, data source, participant information, and prevalence data were extracted from articles using a standard form. Two authors independently evaluated eligibility, and discrepancies were resolved through discussion with the third author. We used random effect models to pool estimates with 95% CIs. Estimated annual percentage change (EAPC) was calculated to assess the temporal trend in prevalence of Parkinson's disease. The study was registered with PROSPERO, CRD42022364417. FINDINGS:83 studies from 37 countries were eligible for analysis, with 56 studies providing all-age prevalence, 53 studies reporting age-specific prevalence, and 26 studies providing both all-age and age-specific prevalence. Global pooled prevalence of Parkinson's disease was 1·51 cases per 1000 (95% CI 1·19-1·88), which was higher in males (1·54 cases per 1000 [1·17-1·96]) than in females (1·49 cases per 1000 [1·12-1·92], p=0·030). During different survey periods, the prevalence of Parkinson's disease was 0·90 cases per 1000 (0·48-1·44; 1980-89), 1·38 cases per 1000 (1·17-1·61; 1990-99), 1·18 cases per 1000 (0·77-1·67; 2000-09), and 3·81 cases per 1000 (2·67-5·14; 2010-23). The EAPC of Parkinson's disease prevalence was significantly higher in the period of 2004-23 (EAPC 16·32% [95% CI 6·07-26·58], p=0·0040) than in the period of 1980-2003 (5·30% [0·82-9·79], p=0·022). Statistically significant disparities in prevalence were observed across six WHO regions. Prevalence increased with HDI or SDI. Considerable variations were observed in the pooled prevalence of Parkinson's disease based on different sample sizes or diagnostic criteria. Prevalence also increased with age, reaching 9·34 cases per 1000 (7·26-11·67) among individuals older than 60 years. INTERPRETATION:The global prevalence of Parkinson's disease has been increasing since the 1980s, with a more pronounced rise in the past two decades. The prevalence of Parkinson's disease is higher in countries with higher HDI or SDI. It is necessary to conduct more high-quality epidemiological studies on Parkinson's disease, especially in low SDI countries. FUNDING:National Nature Science Foundation of China. TRANSLATION:For the Chinese translation of the abstract see Supplementary Materials section.