IDE (rs6583817) polymorphism and pulse pressure are independently and interactively associated with level and change in executive function in older adults.
Bibliographic record
Abstract
OBJECTIVE: We report a gene × environment (health) study focusing on concurrent performance and longitudinal change in a latent-variable executive function (EF) phenotype. Specifically, we tested the independent and interactive effects of a recently identified insulin degrading enzyme genetic polymorphism (IDE rs6583817) and pulse pressure (PP; one prominent aging-related vascular health indicator) across up to 9 years of EF data in a sample of older adults from the Victoria Longitudinal Study. Both factors vary across a continuum of risk-elevating to risk-reducing and have been recently linked to normal and impaired cognitive aging. METHOD: We assembled a genotyped and typically aging group of older adults (n = 599, M age = 66 years at baseline), following them for up to 3 longitudinal waves (M interval = 4.4 years). We used confirmatory factor analyses, latent growth modeling, and path analyses to pursue 3 main research goals. RESULTS: First, the EF single factor model was confirmed comprising 4 executive function tasks and it demonstrated measurement invariance across the waves. Second, older adults with the major IDE G allele exhibited better EF outcomes than homozygotes for the minor A allele at the centering age of 75 years. Adults with higher PP performed more poorly on EF tasks at age 75 years and exhibited greater EF longitudinal decline. Third, gene × health interaction analyses showed that worsening vascular health (higher PP) differentially affected EF performance in older adults with the IDE G allele. CONCLUSION: Genetic interaction analyses can reveal differential and magnifying effects on cognitive phenotypes in aging. In the present case, pulse pressure is confirmed as a risk factor for concurrent and changing cognitive health in aging, but the effects operate differently across the risk and protective allelic distribution of this IDE gene.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".