Methyl nutrients, <scp>DNA</scp> methylation, and cardiovascular disease
Bibliographic record
Abstract
Diet plays an important role in the development and prevention of cardiovascular disease (CVD), but the molecular mechanisms are not fully understood. DNA methylation has been implicated as an underlying molecular mechanism that may account for the effect of dietary factors on the development and prevention of CVD. DNA methylation is an epigenetic process that provides "marks" in the genome by which genes are set to be transcriptionally activated or silenced. Epigenomic marks are heritable but are also responsive to environmental shifts, such as changes in nutritional status, and are especially vulnerable during development. S-adenosylmethionine is the methyl group donor for DNA methylation and several nutrients are required for the production of S-adenosylmethionine. These methyl nutrients include vitamins (folate, riboflavin, vitamin B12, vitamin B6, choline) and amino acids (methionine, cysteine, serine, glycine). As such, imbalances in the metabolism of these nutrients have the potential to affect DNA methylation. The focus of this review is to provide an overview on the current understanding of the relationship between methyl nutrient status and DNA methylation patterns and the potential role of this interaction in CVD pathology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".