Association between LRRK2 gene polymorphisms and Parkinson’s disease progression in a Chinese Han population
Bibliographic record
Abstract
Objective The objective of this study was to investigate the association between LRRK2 gene polymorphisms and Parkinson’s disease (PD) risk, as well as the progression of PD, in a Chinese Han population. Methods A case-control study was carried out on 180 PD patients and 196 healthy controls between October 2019 and October 2023. The demographic and clinical data of all participants were collected. At the baseline and 3-year follow-up, assessment of motor and non-motor symptoms of PD patients were carried out using scales including Unified Parkinson’s Disease Rating Scale parts II, III, and IV, Hoehn and Yahr (H-Y) staging scale, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Non-motor Symptom Questionnaire, Parkinson’s disease sleep scale, Montreal Cognitive Assessment, and Mini-Mental State Examination. Six single nucleotide polymorphisms (SNPs) of the LRRK2 gene rs1994090, rs2046932, rs2708453, rs34778348, rs4768212, and rs7304279 were selected and genotyped using the MassARRAY platform in all participants. Results A strong linkage disequilibrium was observed among the five SNP loci of the LRRK2 gene, including rs1994090, rs2046932, rs2708453, rs4768212, and rs7304279. LRRK2 rs7304279 (OR = 3.572, P < 0.001) and rs34778348 (OR = 0.408, P = 0.003) contributed to the risk of PD development. Carriage of more risk variants were associated with higher risk of PD (OR = 6.467, P < 0.001). Cox proportional hazards model analysis showed that LRRK2 rs7304279 polymorphism was significantly associated with H-Y stage progression (P = 0.030). Conclusion Our findings suggest that LRRK2 rs34778348 and rs7304279 polymorphisms are associated with the risk of developing PD. And LRRK2 rs7304279 polymorphism is correlated with disease progression in PD patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".