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Record W4317870343 · doi:10.1002/mds.29288

Embracing Monogenic Parkinson's Disease: The <scp>MJFF</scp> Global Genetic <scp>PD</scp> Cohort

2023· article· en· W4317870343 on OpenAlexafffund
Eva‐Juliane Vollstedt, Susen Schaake, Katja Lohmann, Shalini Padmanabhan, Alexis Brice, Suzanne Lesage, Christelle Tesson, Marie Vidailhet, Isabel Wurster, F. Hentati, Anat Mirelman, Nir Giladi, Karen Marder, Cheryl Waters, Stanley Fahn, Meike Kasten, Norbert Brüggemann, Max Borsche, Tatiana Foroud, Eduardo Tolosa, Alícia Garrido, Grazia Annesi, Monica Gagliardi, Maria Bozi, Leonidas Stefanis, Joaquim J. Ferreira, Leonor Correia Guedes, Micol Avenali, Simona Petrucci, Lorraine N. Clark, E. Yu. Fedotova, Natalya Abramycheva, Victoria Álvarez, Manuel Menéndez‐González, S. Jesús Maestre, Pilar Gómez‐Garre, Pablo Mir, Andrea Carmine Belin, Caroline Ran, Chin‐Hsien Lin, Ming‐Che Kuo, David Crosiers, Zbigniew K. Wszołek, Owen A. Ross, Joseph Jankovic, Kenya Nishioka, Manabu Funayama, Jordi Clarimón, Caroline H. Williams‐Gray, Marta Camacho, Mario Cornejo‐Olivas, Luis Torres-Ramírez, Yih‐Ru Wu, Guey‐Jen Lee‐Chen, Ana Morgadinho, Teeratorn Pulkes, Pichet Termsarasab, Daniela Berg, Gregor Kuhlenbäumer, Andrea A. Kühn, Friederike Borngräber, Giuseppe De Michele, Anna De Rosa, Alexander Zimprich, Andreas Puschmann, George D. Mellick, Jolanta Dorszewska, Jonathan Carr, Rosangela Ferese, Stefano Gambardella, Bruce A. Chase, Katerina Markopoulou, Wataru Satake, Tatsushi Toda, Malco Rossi, Marcelo Merello, Timothy Lynch, Diana A. Olszewska, Shen‐Yang Lim, Azlina Ahmad‐Annuar, Ai Huey Tan, Bashayer Al‐Mubarak, Haşmet Hanağası, Dariusz Koziorowski, Sibel Ertan, Gençer Genç, Patrícia de Carvalho Aguiar, Melinda Barkhuizen, Márcia Mattos Gonçalves Pimentel, Rachel Saunders‐Pullman, Bart van de Warrenburg, Susan Bressman, Mathias Toft, Silke Appel‐Cresswell, Anthony E. Lang, Matěj Škorvánek, Agnita J.W. Boon, Rejko Krüger, Esther Sammler, Vítor Tumas, Baorong Zhang, Gaëtan Garraux, Sun Ju Chung, Yun Joong Kim, Juliane Winkelmann, Carolyn M. Sue, Eng‐King Tan, Joana Damásio, Péter Klivènyi, Vladimir Kostić, David Arkadir, Mika H. Martikainen, Vanderci Borges, Jens Michael Hertz, Laura Brighina, Mariana Spitz, Oksana Suchowersky, Olaf Rieß, Parimal Das, Brit Mollenhauer, Emilia Gatto, Maria Skaalum Petersen, Nobutaka Hattori, Ruey‐Meei Wu, С. Н. Иллариошкин, Enza Maria Valente, Jan Aasly, Anna Aasly, Roy N. Alcalay, Avner Thaler, Matthew J. Farrer, Kathrin Brockmann, Jean‐Christophe Corvol, Christine Klein

Bibliographic record

VenueMovement Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of AlbertaToronto Western HospitalUniversity of TorontoPacific Centre for Reproductive Medicine
FundersNational Institute of Neurological Disorders and StrokeFaculty of Medicine and Health, University of SydneyDepartment of Medicine, University of TorontoInstituto de Salud Carlos IIIIrving Medical Center, Columbia UniversityNational Institutes of HealthFleniSzegedi TudományegyetemParkinson VerenigingTurun Yliopistollinen KeskussairaalaUniwersytet Medyczny im. Karola Marcinkowskiego w PoznaniuSorbonne UniversitéKoç Üniversitesi Translasyonel Tıp Araştırma MerkeziUniversiteit AntwerpenUniversidade do PortoChang Gung Memorial Hospital, LinkouUniversidade de LisboaNational Taiwan University HospitalTurun YliopistoMedical Research CouncilSociedade Beneficente Israelita Brasileira Albert EinsteinZhejiang UniversityEuropean Regional Development FundUniversitat de BarcelonaCentre National de la Recherche ScientifiqueCentro de Investigación Biomédica en Red sobre Enfermedades NeurodegenerativasUniversitetet i OsloUniversität zu LübeckNational and Kapodistrian University of AthensMahidol UniversityUniversity of AlbertaUniversity of TorontoSagol School of Neuroscience, Tel Aviv UniversityHebrew University of JerusalemUniversità degli Studi di Milano-BicoccaErasmus Universiteit RotterdamMinistry of Science and Technology, TaiwanConsejo Nacional de Investigaciones Científicas y TécnicasKarolinska InstitutetChang Gung UniversityBanaras Hindu UniversityJapan Society for the Promotion of ScienceKing Faisal Specialist Hospital and Research CentreUniversity of UlsanNational Science CouncilNational Taiwan UniversityGriffith UniversityAssistance Publique - Hôpitaux de ParisInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheNational Health and Medical Research CouncilUniversiti MalayaSydney Medical SchoolH. Lundbeck A/SDepartment of Science and Technology, Ministry of Science and Technology, IndiaUniversità degli Studi di PaviaNorthShore University HealthSystemDepartment of Health and Social CareChang Gung Medical FoundationNational Research FoundationNational Institute for Health and Care ResearchNational Taiwan Normal UniversityEuropean CommissionUniversity of TokyoRadboud UniversiteitNorth-West UniversityUniversity of DundeeParkinson's Disease FoundationUniversity College DublinNational Medical Research CouncilHadassah Medical OrganizationUniversity College LondonGlaxoSmithKlineUniversidad de SevillaUniversitätsmedizin GöttingenNIHR Cambridge Biomedical Research CentreInternational Business Machines CorporationMichael J. Fox Foundation for Parkinson's ResearchParkinson's FoundationEU Joint Programme – Neurodegenerative Disease ResearchUniversité du LuxembourgJapan Agency for Medical Research and DevelopmentNHS Education for ScotlandUniversiteit StellenboschParkinsonfondenSingapore General Hospital
KeywordsParkinson's diseaseCohortDiseaseMedicineNeuroscienceGeneticsPsychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: As gene-targeted therapies are increasingly being developed for Parkinson's disease (PD), identifying and characterizing carriers of specific genetic pathogenic variants is imperative. Only a small fraction of the estimated number of subjects with monogenic PD worldwide are currently represented in the literature and availability of clinical data and clinical trial-ready cohorts is limited. OBJECTIVE: The objectives are to (1) establish an international cohort of affected and unaffected individuals with PD-linked variants; (2) provide harmonized and quality-controlled clinical characterization data for each included individual; and (3) further promote collaboration of researchers in the field of monogenic PD. METHODS: We conducted a worldwide, systematic online survey to collect individual-level data on individuals with PD-linked variants in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1, as well as selected pathogenic and risk variants in GBA and corresponding demographic, clinical, and genetic data. All registered cases underwent thorough quality checks, and pathogenicity scoring of the variants and genotype-phenotype relationships were analyzed. RESULTS: We collected 3888 variant carriers for our analyses, reported by 92 centers (42 countries) worldwide. Of the included individuals, 3185 had a diagnosis of PD (ie, 1306 LRRK2, 115 SNCA, 23 VPS35, 429 PRKN, 75 PINK1, 13 DJ-1, and 1224 GBA) and 703 were unaffected (ie, 328 LRRK2, 32 SNCA, 3 VPS35, 1 PRKN, 1 PINK1, and 338 GBA). In total, we identified 269 different pathogenic variants; 1322 individuals in our cohort (34%) were indicated as not previously published. CONCLUSIONS: Within the MJFF Global Genetic PD Study Group, we (1) established the largest international cohort of affected and unaffected individuals carrying PD-linked variants; (2) provide harmonized and quality-controlled clinical and genetic data for each included individual; (3) promote collaboration in the field of genetic PD with a view toward clinical and genetic stratification of patients for gene-targeted clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.250
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations49
Published2023
Admission routes2
Has abstractyes

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