NPJ Parkinson's Disease最新文献

{"title":"SAK3, a Cav3.1 enhancer facilitates mitochondrial function and neuronal survival in Parkinson’s disease models by modulating the SphK1/S1P signaling cascade","authors":"Lina Chen, Jiaqia Ke, Yingqing Wang, Zhiting Chen, Haoling Xu, Xiaochun Chen, En Huang, Guoen Cai, Yuqi Zeng, Qinyong Ye","doi":"10.1038/s41531-026-01520-7","DOIUrl":"https://doi.org/10.1038/s41531-026-01520-7","url":null,"abstract":"Parkinson’s disease (PD) involves dopaminergic neuron loss linked to calcium imbalance. Although calcium channel blockers have shown limited efficacy, SAK3 is a known selective enhancer of the T-type calcium channel Cav3.1. While it shows neuroprotective potential, its precise downstream mechanisms in Parkinson’s disease (PD) remain unclear. We found Cav3.1, the principal T-type channel in the substantia nigra, is downregulated in PD models and patient tissues. In a pretreatment paradigm, SAK3 restored Cav3.1 function, improving motor deficits, neuronal survival, and mitochondrial integrity. Metabolomic analysis revealed SAK3 drives metabolic shifts toward glycine/serine pathways and enriches mitochondrial oxidative phosphorylation genes. Mechanistically, SAK3 promotes calcium influx, activating the SphK1/S1P/Akt pathway to enhance mitochondrial function—stabilizing membrane potential, boosting oxidative phosphorylation, and rebalancing apoptosis regulators. Collectively, our study suggests that the effects of SAK3 are associated with Cav3.1 activationl and it orchestrates a protective response in preventive PD models by engaging the SphK1/S1P signaling axis, thereby stabilizing mitochondria and inhibiting apoptosis. These findings reveal a potential downstream effector pathway associated with Cav3.1 activation and position the SphK1/S1P network as a promising therapeutic target for further investigation in PD.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"59 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author Correction: Cryopreservable dopaminergic progenitors derived from human iPSCs with accelerated loss of pluripotency and early functional restoration in Parkinsonian rats.","authors":"Chia-Yu Chang, Cheng-Yun Chiang, Hsiao-Chien Ting, Yung-Jen Tsai, Po-Chiang Chan, Mei-Fang Chen, Yun-Ting Guo, Yuan-Hsuan Liu, Yu-Shuan Chen, Chi-Hsuan Chuang, Po-Cheng Lin, Lin-Hsiang Chuang, Mao-Hsuan Huang, Yi-Chung Tung, Po-Yi Chen, Horng-Jyh Harn, Hong-Lin Su, Shinn-Zong Lin","doi":"10.1038/s41531-026-01569-4","DOIUrl":"10.1038/s41531-026-01569-4","url":null,"abstract":"","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"12 1","pages":""},"PeriodicalIF":8.5,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated microRNA–protein plasma biomarkers improve discrimination of multiple system atrophy from Parkinson’s disease","authors":"Hsiang-Hsuan Lin-Wang, Yi-Tzang Tsai, Jing-Wen Huang, Ming-Che Kuo, Yung-Tsai Chu, Chia-Chen Lu, Yan-Hong Chen, Frederick Kin Hing Phoa, Pin-Jui Kung, Elisha Wei-Chun Ho, Charles Pin-Kuang Lai, Chien-Tai Hong, Seong Wook Yang, Chai-Ching Wu, Koji Ueda, Takahiro Ochiya, Ruey-Meei Wu, Shau-Ping Lin","doi":"10.1038/s41531-026-01538-x","DOIUrl":"https://doi.org/10.1038/s41531-026-01538-x","url":null,"abstract":"Multiple system atrophy (MSA) is a fatal, rapidly progressive atypical parkinsonism that responds poorly to Parkinson’s disease (PD) medications, and diagnostic precision remains a critical unmet need. Earlier, accurate diagnosis would give patients realistic prognostic expectations and enable timely clinical-trial enrollment. We sought plasma biomarkers discriminating MSA from PD. Plasma small RNA sequencing and extracellular vesicle proteomics in discovery cohorts, filtered by the Biomedical Oriented Logistic Dantzig (BOLD) selector, nominated two microRNAs (hsa-miR-520a-5p, hsa-miR-22-3p) and two proteins (LCAT, kallistatin). Candidates were quantified in independent cohorts by XENO-Q qPCR and sandwich ELISA, and multivariate logistic regression models including all pairwise interactions were trained (N = 65) and validated in an independently recruited testing cohort (MSA N = 22; PD N = 16). The combined microRNA–protein model achieved test-set AUC 0.813 (95% CI 0.654–0.971), with sensitivity 0.818 and specificity 0.750, outperforming microRNA-only (AUC 0.599) and protein-only (AUC 0.747) models and indicating complementary rather than redundant information. The markers converge on pathways of glycerophospholipid metabolism, cholesterol homeostasis, myelin integrity, and inflammatory regulation, consistent with the oligodendrocyte pathology central to MSA. These exploratory findings show that a non-invasive plasma multi-analyte panel can differentiate MSA from PD with clinically meaningful accuracy, and support prospective validation in larger cohorts.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"28 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-based quantification of leg agility in Parkinson’s disease and its relationship to upper extremity motor impairments","authors":"Tahereh Zarrat Ehsan,Michael Tangermann,King Chung Ho,Bastiaan R. Bloem,Luc J. W. Evers","doi":"10.1038/s41531-026-01558-7","DOIUrl":"https://doi.org/10.1038/s41531-026-01558-7","url":null,"abstract":"Abstract Bradykinesia is a hallmark sign of Parkinson’s disease (PD). Current clinical assessment is susceptible to inter-rater variability and only provides a single, ordinal severity score. Here, we propose a computer vision-based framework to quantify distinct motor characteristics from video recordings of the leg agility test, including slowness (bradykinesia), reduced amplitude (hypokinesia), progressive decrement (sequence effect) and irregularity (hesitation-halts). We validate our approach using a large-scale dataset of 3097 video recordings captured from 443 participants in the Personalized Parkinson Project. We demonstrated that features reflecting bradykinesia, hypokinesia and hesitation-halts differed significantly across clinical severity ratings, whereas sequence effect features showed no consistent differences. Furthermore, data-driven analysis using principal component analysis with varimax rotation revealed that two additional feature dimensions (i.e., beyond the four conventional domains) may be necessary to fully characterize motor abnormalities during the leg agility test. We also explored the relationship between finger-tapping (a distal upper limb task) and leg agility (a proximal lower extremity task). The analyses showed that upper and lower limb tasks provide differential insights into PD motor impairment, and that asymmetry between the least and most affected side was more pronounced in the upper extremities. This work highlights the ability of video-based assessment to provide objective characterization of motor impairment in PD, with the potential to support both in-clinic evaluations and at-home remote monitoring. Future work will investigate responsiveness to medication and longitudinal disease progression.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"40 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The exposome and Parkinson’s disease: the role of air pollution and socioeconomic status—a systematic review","authors":"Aislinn R. Taylor,Allyson E. Missling,Tom C. Russ,Rejko Krüger,Morgan D. Leonard,Anja Leist","doi":"10.1038/s41531-026-01559-6","DOIUrl":"https://doi.org/10.1038/s41531-026-01559-6","url":null,"abstract":"Abstract There is an unclear relationship between socioeconomic status (SES)—a social exposure—and PD risk, where those with a higher SES may be more at risk, or the inverse may be true. The risk of PD may not be directly related to SES, but exposures like outdoor air pollution may be involved, particularly as associations between SES and air pollution exposure, and between air pollution exposure and neurodegenerative outcomes have been noted. Scopus, PubMed, and PsycINFO were searched to identify studies analysing degree of exposure to chronic ambient outdoor air pollution and incidence of PD, or the association between individual measures of SES and degree of exposure to outdoor air pollution. 25,416 studies were screened, with 37 fitting inclusion criteria. Meta-analyses were conducted by air pollutant, with narrative syntheses when this was not possible. There was evidence of a positive association between exposure to NO 2 and PM 2.5 , and PD incidence. Higher and lower SES were associated with greater air pollution exposure, varying by location. The review had a low certainty of evidence, according to GRADE. Exposure to air pollution may be associated with greater risk of PD, and SES may play a role in the degree of air pollution exposure.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"84 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteo-metabolomic integration identifies stage-specific candidate biomarkers for Parkinson's disease.","authors":"Aya Galal, Ahmed Moustafa, Mohamed Salama","doi":"10.1038/s41531-026-01497-3","DOIUrl":"10.1038/s41531-026-01497-3","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a progressive neurodegenerative disorder with a prolonged prodromal phase and complex motor symptoms. Despite improved clinical criteria, early diagnosis and longitudinal monitoring remain challenging. While cerebrospinal fluid (CSF) and plasma metabolites and proteins show biomarker potential, their utility in predictive models is insufficiently characterized. We employed a secondary computational approach to integrate proteometabolomic profiles from CSF and plasma samples of >1100 Parkinson's Progression Markers Initiative (PPMI) participants. Using multi-omics machine learning, we identified biofluid-specific signatures and evaluated predictive performance. Twenty-one biomarker candidates were validated across three models (SVM, GLMNET, RF); SVM and GLMNET achieved the highest recall (83-86%) and AUCs of 0.84-0.89. Longitudinal mixed-effects modeling revealed eight candidates associated with progression across diagnostic stages. We identified a three-part molecular framework characterizing neurodegeneration: a diagnostic subpanel reflecting early microbiome dysregulation (secretory granins and metabolites) and synaptic breakdown; a second subpanel monitoring phenoconversion via neurogenesis precursors and extracellular matrix proteins; and a third subpanel tracking progression through chronic neuroinflammation and immune activation. This integrated multi-omics approach provides a robust framework for stage-specific PD monitoring and potential clinical deployment.</p>","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"12 1","pages":""},"PeriodicalIF":8.5,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward brain-derived extracellular vesicles as biochemical biopsies of synucleinopathies","authors":"Cristina A. Weber,Oscar D. Bello","doi":"10.1038/s41531-026-01563-w","DOIUrl":"https://doi.org/10.1038/s41531-026-01563-w","url":null,"abstract":"Abstract Synucleinopathies are defined by pathological α-synuclein states, including conformation, aggregation, modification, and seeding competence, that abundance alone cannot capture. Extracellular vesicles may preserve these states during transit from brain to blood, but mechanistic plausibility, diagnostic discrimination, and brain provenance are distinct claims. We argue that proteomics can interrogate vesicle identity, cargo and proteoforms, while unresolved provenance, pre-analytical variability, and incomplete clinical validation leave the biochemical biopsy concept plausible but unvalidated.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"15 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable machine learning reveals the challenges of predicting new-onset motor complications in Parkinson’s disease","authors":"Walter Endrizzi,Nicole Campese,Flavio Ragni,Monica Moroni,Stefano Bovo,Chiara Longo,Lorenzo Gios,Antonio Uccelli,Bruno Giometto,Giuseppe Jurman,Venet Osmani,Maria Chiara Malaguti,the NeuroArtP3 Network,Andrea Falini,Antonella Castellano,Andrea Rossi,Domenico Tortora,Costanza Parodi,Antonio Verrico,Federica Sabatini,Emilio Portaccio,Matteo Betti,Guido Pasquini,Filippo Gerli,Claudia Niccolai,Isabella Cama,Cristina Campi,Alessio Cirone,Sara Garbarino,Michele Piana,Donatella Ottaviani,Raffaella Di Giacopo,Ruggero Bacchin","doi":"10.1038/s41531-026-01550-1","DOIUrl":"https://doi.org/10.1038/s41531-026-01550-1","url":null,"abstract":"","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"30 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable 3D multi-scale movement quantification automates motor assessments and estimates Parkinson’s disease duration","authors":"Kyungdo Kim,Yuxuan Wen,Lauren Lim,Sihan Lyu,Yi Shi,Kyle T. Mitchell,Timothy W. Dunn","doi":"10.1038/s41531-026-01512-7","DOIUrl":"https://doi.org/10.1038/s41531-026-01512-7","url":null,"abstract":"Abstract Quantitative assessment of motor impairment in Parkinson’s disease (PD) remains limited, particularly in tracking how deficits evolve over time. Current bedside scoring systems, including the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), capture clinically important observations but condense them into categorical, semi-subjective ratings. This manual scoring process is imprecise, limiting diagnostic and prognostic accuracy and hindering our understanding of PD impairments and their progression. While there have been recent efforts to improve scoring via sensor-based measurements and machine learning, these approaches have limitations impeding their utility and clinical adoption. In principle, video-based methods enable comprehensive quantification and monitoring of motor function, but previous approaches largely have used imprecise 2D movement features, analyzed only individual motor tasks, and not addressed disease progression. Here, we introduce an interpretable, quantitative framework built on a synchronized, markerless 3D pose tracking system. We recorded three routinely performed MDS-UPDRS motor tasks from a large cohort of patients with PD and healthy subjects, and extracted task-level, clinically explainable kinematic features. These activities probed complementary motor subsystems—fine hand, forearm rotation, and whole-body locomotion—offering a concise yet comprehensive view of PD motor function. From 3D poses, we developed how movement patterns differed between PD and healthy subjects and how these patterns shifted as PD progressed to form distinct movement characteristics. Using machine learning models combining 3D motor features from multiple activities across multiple spatiotemporal scales, we also automatically classified diagnostic status, duration-defined PD severity, and inferred time since diagnosis. These results establish a transparent digital-biomarker framework that may support future longitudinal monitoring of PD in clinics and trials.","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"43 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial analysis of the association of cardiometabolic diseases and incident Lewy body dementia in the Medicare population","authors":"George K. Karway,Brittany Krzyzanowski,Jordan A. Killion,Irene M. Faust,Osvaldo J. Laurido-Soto,Brad A. Racette","doi":"10.1038/s41531-026-01557-8","DOIUrl":"https://doi.org/10.1038/s41531-026-01557-8","url":null,"abstract":"","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"159 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}