MDSGene update and expansion: Clinical and genetic spectrum of <i>LRRK2</i> variants in Parkinson’s disease
Bibliographic record
Abstract
ABSTRACT Pathogenic variants in the LRRK2 gene are one of the most commonly identifiable monogenic causes of Parkinsońs disease (PD, PARK-LRRK2). This systematic MDSGene literature review comprehensively summarizes published demographic, clinical, and genetic findings related to potentially pathogenic LRRK2 variants ( https://www.mdsgene.org/ ). Recent insights on LRRK2’s kinase activity have been incorporated for pathogenicity scoring. Data on 7,885 individuals with 292 different variants were curated, including 3,296 patients with PD carrying 205 different potentially disease-causing LRRK2 variants. The initial MDSGene review covered only 724 patients carrying 23 different LRRK2 variants. Missingness of phenotypic data in the literature was high, hampering the identification of detailed genotype-phenotype correlations. Notably, the median age at onset in the patients with available information was 56 years, with approximately one-third having PD onset <50 years. Tremor was the most frequently reported initial symptom and more frequent than reported in other dominantly inherited forms of PD. Of the 205 potentially disease-causing variants, 14 (6.8%) were classified as pathogenic, 8 (3.9%) as likely pathogenic, and the remaining 183 (89.3%) as variants of uncertain significance (VUS). The pathogenic p.G2019S variant was the most frequent pathogenic variant, followed by p.R1441G and p.R1441C, accounting for >80% of patients, with Tunisia, Spain, and Italy contributing about half of patients. This systematic review represents the largest database on PARK-LRRK2 to date and provides an important resource to improve precision medicine. Given their high frequency, a better interpretation of the pathogenicity of VUS is needed for selection and stratification of patients in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".