Do Epigenetic Pathways Initiate Late Onset Alzheimer Disease (LOAD): Towards a New Paradigm
Bibliographic record
Abstract
Late onset Alzheimer's disease (LOAD) is a non-familial, progressive neurodegenerative disease and the most prominent form of dementia in the elderly. Accumulating evidence suggests that LOAD not only results from the combined effects of variation in a number of genes and environmental factors, but also from epigenetic abnormalities such as histone modifications or DNA methylation. In comparison to monogenic diseases, LOAD exhibits numerous anomalies that suggest an epigenetic component in disease etiology. Evidence against a monogenic course and for an epigenetic component include: 1) the dominance of sporadic cases over familial ones and the low estimated concordance rates for monozygotic twins; 2) gender specific susceptibility and course of disease; 3) parent-of-origin effects, and late age of onset; 4) brain chromatin abnormalities, non-Mendelian inheritance patterns, and atypical levels of folate and homocysteine; and 5) monoallelic expression patterns of susceptibility genes [1]. The epigenome is particularly susceptible to deregulation during early embryonic and neonatal periods and thus disturbances during these periods can have latent lasting effects. The Latent Early-life Associated Regulation (LEARn) model attempts to explain these consequences from a brain specific point of view. In the present review we present the evidence that support the role of epigenetics in the development of AD and explore the potential pathways and mechanisms that may be involved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".