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Record W2787157191 · doi:10.1136/jnnp-2017-317348

Features of <i>GBA</i>-associated Parkinson’s disease at presentation in the UK <i>Tracking Parkinson’s</i> study

2018· article· en· W2787157191 on OpenAlexaboutno aff
Naveed Malek, Rimona S. Weil, Catherine Bresner, Michael Lawton, Katherine A. Grosset, Manuela Tan, Nin Bajaj, Roger A. Barker, David J. Burn, Thomas Foltynie, John Hardy, Nicholas Wood, Yoav Ben‐Shlomo, Nigel Williams, Donald G. Grosset, Huw R. Morris

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2018
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsnot available
FundersUCB PharmaNIHR Newcastle Biomedical Research CentreMedical Research CouncilIpsenH. Lundbeck A/SIpsen FundRosetrees TrustNewcastle UniversityNeurosciences FoundationAcorda TherapeuticsBrain Research TrustTeva Pharmaceutical IndustriesNational Institute for Health and Care ResearchEisaiMotor Neurone Disease AssociationParkinson's UKEvelyn TrustAcademy of Medical SciencesNewcastle upon Tyne Hospitals NHS Foundation TrustWellcome TrustGlaxoSmithKline
KeywordsGlucocerebrosidaseParkinson's diseaseDiseaseInternal medicineMedicineMovement disordersCohortCognitionDementiaOncologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Objectives To examine the influence of the glucocerebrosidase (GBA) mutation carrier state on age at onset of Parkinson’s disease (PD), the motor phenotype and cognitive function at baseline assessment in a large cohort of UK patients. We also analysed the prevalence of mood and behavioural problems that may confound the assessment of cognitive function. Methods We prospectively recruited patients with PD in the Tracking Parkinson’s study. We fully sequenced the GBA gene in all recently diagnosed patients (≤3.5 years). We examined cognitive (Montreal Cognitive Assessment) and motor (Movement Disorder Society Unified Parkinson’s Disease Rating Scale part 3) function at a baseline assessment, at an average of 1.3 years after diagnosis. We used logistic regression to determine predictors of PD with mild cognitive impairment and PD with dementia. Results We studied 1893 patients with PD: 48 (2.5%) were heterozygous carriers for known Gaucher’s disease (GD) causing pathogenic mutations; 117 (6.2%) had non-synonymous variants, previously associated with PD, and 28 (1.5%) patients carried variants of unknown significance in the GBA gene. L444P was the most common pathogenic GBA mutation. Patients with pathogenic GBA mutations were on average 5 years younger at disease onset compared with non-carriers (P=0.02). PD patients with GD-causing mutations did not have an increased family risk of PD. Patients with GBA mutations were more likely to present with the postural instability gait difficulty phenotype compared with non-carriers (P=0.02). Patients carrying pathogenic mutations in GBA had more advanced Hoehn and Yahr stage after adjustment for age and disease duration compared with non-carriers (P=0.005). There were no differences in cognitive function between GBA mutation carriers and non-carriers at this early disease stage. Conclusions Our study confirms the influence of GBA mutations on the age of onset, disease severity and motor phenotype in patients with PD. Cognition did not differ between GBA mutation carriers and non-carriers at baseline, implying that cognitive impairment/dementia, reported in other studies at a later disease stage, is not present in recently diagnosed cases. This offers an important window of opportunity for potential disease-modifying therapy that may protect against the development of dementia in GBA-PD. Clinical trial registration NCT02881099; Results.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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".

Quick stats

Citations143
Published2018
Admission routes1
Has abstractyes

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