Epigenetic effects of folate and related B vitamins on brain health throughout life: Scientific substantiation and translation of the evidence for health improvement strategies
Bibliographic record
Abstract
and riboflavin) can perturb one-carbon metabolism and adversely affect brain development in early life and brain function in later life. Human studies show that maternal folate status during pregnancy is associated with cognitive development in the child, whilst optimal B vitamin status may help to prevent cognitive dysfunction in later life. The biological mechanisms explaining these relationships are not clear but may involve folate-related DNA methylation of epigenetically controlled genes related to brain development and function. A better understanding of the mechanisms linking these B vitamins and the epigenome with brain health at critical stages of the lifecycle is necessary to support evidence-based health improvement strategies. The EpiBrain project, a transnational collaboration involving partners in the United Kingdom, Canada and Spain, is investigating the nutrition-epigenome-brain relationship, particularly focussing on folate-related epigenetic effects in relation to brain health outcomes. We are conducting new epigenetics analysis on bio-banked samples from existing well-characterised cohorts and randomised trials conducted in pregnancy and later life. Dietary, nutrient biomarker and epigenetic data will be linked with brain outcomes in children and older adults. In addition, we will investigate the nutrition-epigenome-brain relationship in B vitamin intervention trial participants using magnetoencephalography, a state-of-the-art neuroimaging modality to assess neuronal functioning. The project outcomes will provide an improved understanding of the role of folate and related B vitamins in brain health, and the epigenetic mechanisms involved. The results are expected to provide scientific substantiation to support nutritional strategies for better brain health across the lifecycle.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".