Diet, genes and disease: implications for nutrition policy
Bibliographic record
Abstract
There is extensive evidence to show that there is considerable variation in diet and disease patterns in Europe and that many of the dietary patterns are predictive of chronic disease. Increasingly, there is evidence that this dietary effect is mediated by genetic background. The present paper examines the role of polymorphisms within three genes, those responsible for the synthesis of apoE, 5,10-methylenetetrahydrofolate reductase (MTHFR) and PPARgamma. There is clear evidence to support the concept that the diet-disease link is moderated by genetic variation. The paper then considers whether this moderating effect will have implications for dietary recommendations. In the formulation of dietary reference values it has long been recognized that these values cannot cover the needs of all individuals. By setting the upper level at the mean value +2 sd, the needs of 97.5% of the population are covered. Setting a hypothetical scenario of a nutrient requirement of 200 mg/d and a polymorphism with an allelic frequency in the general population in the range of 0, 10, 20 and 30% that causes an increased nutrient requirement of 25%, there was no evidence that the traditional approach requires revision. Whilst it is recognized that genetic variability may not influence population goals, genetic variability will have to be taken into account in the clinical nutrition management of disease. To knowingly assign a patient to life-long treatment with a diet that for genetic reasons will have no success is both unethical and uneconomical. Once accepted in clinical nutrition, the diet-gene interaction will filter into the prevention of disease in public health nutrition.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.022 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 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".