Vascular Health and Genetic Risk Affect Mild Cognitive Impairment Status and 4-Year Stability: Evidence From the Victoria Longitudinal Study
Bibliographic record
Abstract
OBJECTIVES: Mild cognitive impairment (MCI) is a high-risk condition for progression to Alzheimer's disease (AD). Vascular health is a key mechanism underlying age-related cognitive decline and neurodegeneration. AD-related genetic risk factors may be associated with preclinical cognitive status changes. We examine independent and cross-domain interactive effects of vascular and genetic markers for predicting MCI status and stability. METHOD: We used cross-sectional and 2-wave longitudinal data from the Victoria Longitudinal Study, including indicators of vascular health (e.g., reported vascular diseases, measured lung capacity and pulse rate) and genetic risk factors-that is, apolipoprotein E (APOE; rs429358 and rs7412; the presence vs absence of ε4) and catechol-O-methyltransferase (COMT; rs4680; met/met vs val/val). We examined associations with objectively classified (a) cognitive status at baseline (not impaired congnitive (NIC) controls vs MCI) and (b) stability or transition of cognitive status across a 4-year interval (stable NIC-NIC vs chronic MCI-MCI or transitional NIC-MCI). RESULTS: Using logistic regression, indicators of vascular health, both independently and interactively with APOE ε4, were associated with risk of MCI at baseline and/or associated with MCI conversion or MCI stability over the retest interval. DISCUSSION: Several vascular health markers of aging predict MCI risk. Interactively, APOE ε4 may intensify the vascular health risk for MCI.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".