Nutrigenomics and Personalized Diet: What are the Anticipated Impacts for Research on Chronic Diseases and Public Health?
Bibliographic record
Abstract
With parallel advances in genomics and in nutrition research, a new hybrid science has emerged at their intersection, often referred to as nutritional genomics which includes nutrigenomics and nutrigenetics. While the operational definitions will continue to evolve as nutritional genomics matures as a new field of inquiry, a central tenet will likely be on ways in which the human genome interacts with nutritional exposures. This emerging form of science has considerable implications for research in biomedicine and for public health. The progress in nutrigenomics research will likely increase our understanding of chronic disease etiology and the relationship between nutrients and common complex diseases. The potential for nutritional genomics in chronic disease prevention is also of great interest given that diet is a modifiable risk factor and because of the marked interindividual variability in response to the diet. The inclusion of genetic information as part of an overall strategy to improve the population health will be critical as more genomic data accumulate in nutrition science and used to develop recommendations for specific dietary requirements based on individual genetic make-up. In addition, use of genetic information from companies offering at-home direct-to-consumer nutrigenomics tests may have significant consequences for the population health and how we perceive and relate to food and other members of the society depending on human genetic variation. These implications of nutrigenomics and nutrigenetics such as the translation of research into public health practice will be discussed. Another dimension of interest is the need for well-trained health professionals. To this end, registered dietitians are essential to the efforts for incorporation of genomics into public health. Finally, future perspectives of nutrigenomics and nutrigenetics will be examined, with a view to how best to integrate the nascent field of nutrigenomics with established public health research and practices. Keywords: Nutrigenomics, chronic disease, public health, preventive medicine, personalized diet
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".