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Record W2134829539 · doi:10.1016/s1474-4422(08)70117-0

Phenotype, genotype, and worldwide genetic penetrance of LRRK2-associated Parkinson's disease: a case-control study

2008· article· en· W2134829539 on OpenAlexaff
Daniel G. Healy, Mario Falchi, Sean S. O’Sullivan, Vincenzo Bonifati, Alexandra Durr, Susan Bressman, Alexis Brice, Jan Aasly, Cyrus P. Zabetian, Stefano Goldwurm, Joaquim J. Ferreira, Eduardo Tolosa, Denise M. Kay, Christine Klein, David R. Williams, Connie Marras, Anthony E. Lang, Zbigniew K. Wszołek, José Berciano, Anthony H.V. Schapira, Timothy Lynch, Kailash P. Bhatia, Thomas Gasser, Andrew J. Lees, Nicholas Wood

Bibliographic record

VenueThe Lancet Neurology · 2008
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute on AgingNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institutes of HealthFondazione Grigioni per il Morbo di ParkinsonAgence Nationale de la RechercheVolkswagen FoundationBrain Research TrustMayo ClinicParkinson's UKMichael J. Fox Foundation for Parkinson's Research
KeywordsLRRK2PenetranceDiseaseDyskinesiaMutationMedicineGenotypeParkinson's diseaseInternal medicineMutation frequencyGeneticsPhenotypeBiologyGene

Abstract

fetched live from OpenAlex

BACKGROUND: Mutations in LRRK2, the gene that encodes leucine-rich repeat kinase 2, are a cause of Parkinson's disease (PD). The International LRRK2 Consortium was established to answer three key clinical questions: can LRRK2-associated PD be distinguished from idiopathic PD; which mutations in LRRK2 are pathogenic; and what is the age-specific cumulative risk of PD for individuals who inherit or are at risk of inheriting a deleterious mutation in LRRK2? METHODS: Researchers from 21 centres across the world collaborated on this study. The frequency of the common LRRK2 Gly2019Ser mutation was estimated on the basis of data from 24 populations worldwide, and the penetrance of the mutation was defined in 1045 people with mutations in LRRK2 from 133 families. The LRRK2 phenotype was defined on the basis of 59 motor and non-motor symptoms in 356 patients with LRRK2-associated PD and compared with the symptoms of 543 patients with pathologically proven idiopathic PD. FINDINGS: Six mutations met the consortium's criteria for being proven pathogenic. The frequency of the common LRRK2 Gly2019Ser mutation was 1% of patients with sporadic PD and 4% of patients with hereditary PD; the frequency was highest in the middle east and higher in southern Europe than in northern Europe. The risk of PD for a person who inherits the LRRK2 Gly2019Ser mutation was 28% at age 59 years, 51% at 69 years, and 74% at 79 years. The motor symptoms (eg, disease severity, rate of progression, occurrence of falls, and dyskinesia) and non-motor symptoms (eg, cognition and olfaction) of LRRK2-associated PD were more benign than those of idiopathic PD. INTERPRETATION: Mutations in LRRK2 are a clinically relevant cause of PD that merit testing in patients with hereditary PD and in subgroups of patients with PD. However, this knowledge should be applied with caution in the diagnosis and counselling of patients. FUNDING: UK Medical Research Council; UK Parkinson's Disease Society; UK Brain Research Trust; Internationaal Parkinson Fonds; Volkswagen Foundation; National Institutes of Health: National Institute of Neurological Disorders and Stroke and National Institute of Aging; Udall Parkinson's Disease Centre of Excellence; Pacific Alzheimer Research Foundation Centre; Italian Telethon Foundation; Fondazione Grigioni per il Morbo di Parkinson; Michael J Fox Foundation for Parkinson's Research; Safra Global Genetics Consortium; US Department of Veterans Affairs; French Agence Nationale de la Recherche.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations1,563
Published2008
Admission routes1
Has abstractyes

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