Abstract 500: Assessing the Impact of Cardiovascular Disease-Related Genetic Variation on Clinical Cognitive Impairment
Bibliographic record
Abstract
Introduction: “Cognitive impairment, no dementia” (CIND) is a prodromal stage of cognitive decline which marks the onset of dementia and is due most commonly to 1) Alzheimer disease (AD) or 2) vascular dysfunction. In order to assess the genetic component of CIND susceptibility, we investigated cardiovascular disease (CVD) and AD-associated genetic variation in CIND patients, hypothesizing that the genetic variation affecting CVD and AD susceptibility is also associated with CIND susceptibility. Methods: Our study cohort was taken from the Canadian Study of Health and Aging (CSHA) and was comprised of patients >65 years old with CIND (n=274) and matched normal controls (n=301). We genotyped ∼200,000 CVD-related SNPs using the Cardio-Metabochip genotyping array (Illumina). We also genotyped a panel of the top 11 AD-associated single nucleotide polymorphisms (SNPs) and APOE isotype. We tested for association between CIND status and genotypes using a logistic regression model adjusted for appropriate covariates. Genetic risk scores (GRSs) evaluated associations between CIND status and the accumulation of multiple genetic markers. Results: From our Cardio-Metabochip analysis, we identified 5 novel CIND susceptibility loci, with rs16901621 in FLJ22536 as our most significantly associated SNP (P=1.05E-06, OR=2.51, 95%CI=1.73-3.63). APOE ε4 isotype was modestly associated with CIND status (P<0.05), while AD SNP risk alleles were not associated (P>0.1). Conclusion: Using a high-throughput CVD microarray, we found novel genetic markers for CIND approaching genome-wide levels of significance. In contrast, known genetic markers for AD, such as APOE ε4 showed only modest associations in this cohort. Follow-up of variants in a CSHA replication cohort (n=370) is currently underway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".