COMPLEX INTERACTIONS BETWEEN INFLAMMATION AND METABOLIC HORMONES DURING AGING
Bibliographic record
Abstract
Introduction: We recently found that higher neurodegenerative genetic risk exacerbates the deleterious effects of type 2 diabetes on aging-related neurocognitive slowing.We now examine whether favorable genotypes of neurodegenerative genes will independently or interactively (with vascular health) predict preserved executive function (EF) levels and trajectories in older adults.Using Victoria Longitudinal Study (VLS) data, we test two key Alzheimer's-risk genes (ApoE, rs429358, rs7412; PICALM, rs541548) for potential synergy with pulse pressure (PP).Method: This VLS subsample was based on ApoE genotyping (n=219, M age=70, range=53-90), contrasting risk-reducing e2+ with risk-elevating e4+ groups.Latent growth modeling was applied in an accelerated longitudinal design over a 40-year band of aging with up to 3 waves of EF performance.We systematically examined combinations of cumulative low versus high-risk from ApoE and PICALM, as supplemented similarly by CLU (rs11136000) and CR1 (rs6656401).Lower PP represented healthier vasculature.Results: First, both ApoE (low-risk combination, b=-.076, p=.548; high-risk combination, b=-.251, p=.004) and PICALM (low-risk, b=-.233, p=.164; highrisk b=-.325, p=.015) x PP models showed that lower genetic risk enhanced the beneficial effects of lower PP on EF.Second, models testing two genetic composites (ApoE or PICALM [plus CLU and CR1]) x PP interactions provided important clarification.For both composites (lowrisk p>.05; high-risk p<.05), lower genetic risk magnified the beneficial effects of healthy PP on EF performance and longitudinal stability.Conclusion: "Successful" cognitive aging may be represented by long-term longitudinal stability.Such non-decline patterns may be promoted through synergies produced by risk-reducing biological (genetic) and health (vascular) factors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".