P3‐103: ATP5H, ZCCHC14, AND PRKCH AS GENETIC RISK FACTORS FOR ALZHEIMER DISEASE AND VASCULAR DEMENTIA
Bibliographic record
Abstract
Current evidence suggests that Alzheimer disease (AD) and cerebrovascular disease (CVD) share a number of risk factors, including the well known vascular risks such as hypertension, diabetes, hypercholesterolemia, and obesity. Recent genome wide association studies revealed several novel risk loci for cerebrovascular diseases, in particular small vessels white matter disease, which often co-exists with AD. In the current study, we undertook a genetic case-control study from 3 Canadian cohorts to examine the role of ATP5H, ZCCHC14, and PRKCH in AD and vascular dementia (VaD). Patients diagnosed with AD or VaD were genotyped and compared to ethnicity-matched participants with no cognitive impairment (NCI controls). Genotypes were obtained using TaqMan assays. Allele and genotype frequencies were compared using a chi-squared test and the odds ratio was calculated using logistic regression, adjusting for covariates such as age, sex, and APOE genotype. There were 636 controls, 667 AD, and 118 VaD cases in the combined cohort. In the unadjusted analysis, there was no significant association with the risk genes to AD or VaD. When stratified by APOE carrier status, we found evidence of association between PRKCH with AD (p=0.023), but not VaD (P=0.462), in non APOE-e4 carriers. Our findings suggest that PRKCH may modulate risk of AD in non-APOE-e4 carriers. Lack of power may affect the ability to detect association with VaD. Larger scale studies and further investigations between vascular risks and AD may provide novel target for therapeutic intervention.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".