Genomic Association Study for Cognitive Impairment in Parkinson's Disease
Bibliographic record
Abstract
Background: Cognitive impairment is very common in Parkinson's disease (PD) and constitutes the most debilitating complication of this disease. However, to date, few studies have investigated a genome-wide association in the development of cognitive impairment of PD. We aimed to identify the genetic loci associated with cognitive impairment in patients with sporadic PD by ethnicity-specific genotyping. Materials and methods: We recruited 1,070 patients with PD and performed a genome-wide association study using the Korean Chip, a microarray chip containing 827,400 single-nucleotide polymorphisms (SNPs) optimized for the Korean population. Multiple logistic regression models adjusting for age, sex, years of education, and disease duration were used to compare between patients with and without cognitive impairment, which was defined using the Mini-Mental Status Examination (MMSE) score (MMSE score ≥ 26 vs. < 26) or the Montreal Cognitive Assessment (MoCA) score (MoCA score ≥24 vs. < 24). Results:RYR2 SNP rs10495397 was most significantly associated with cognitive impairment based on the MMSE scores (OR = 3.21; 95% CI = 1.96–5.25, P = 3.36 × 10−6) and CASC17 showed the strongest association with cognitive impairment based on the MoCA scores. However, none of the SNPs were statistically significant after Bonferroni correction. Conclusion:RYR2 may play a role in cognitive impairment in PD by the pathogenic mechanism of neuroinflammation. However, more studies are needed to replicate and validate the results of our functional study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".