Integrating Anticipated Nutrigenomics Bioscience Applications with Ethical Aspects
Bibliographic record
Abstract
Nutrigenomics is a subspecialty of nutrition science which aims to understand how gene-diet interactions influence individuals' response to food, disease susceptibility, and population health. Yet ethical enquiry into this field is being outpaced by nutrigenomics bioscience. The ethical issues surrounding nutrigenomics face the challenges of a rapidly evolving field which bring forward the additional dimension of crossdisciplinary integrative research between social and biomedical sciences. This article outlines the emerging nutrigenomics definitions and concepts and analyzes the existing ethics literature concerning personalized nutrition and presents "points to consider" over ethical issues regarding future nutrigenomics applications. The interest in nutrigenomics coincides with a shift in emphasis in medicine and biosciences toward prevention of future disease susceptibilities rather than treatment of already established disease. Hence, unique ethical issues emerge concerning the extent to which nutrigenomics can alter our relation to food, boundaries between health and disease, and the folklore of medical practice. Nutrigenomics can result in new social values, norms, and responsibilities for both individuals and societies. Nutrigenomics is not only another new application of "-omics" technologies in the context of gene-diet interactions. Nutrigenomics may fundamentally change the way we perceive human illness while shifting the focus and broadening the scope of health interventions from patients to healthy individuals. In resource- and time-limited healthcare settings, this creates unique ethical dilemmas and distributive justice issues. Ethical aspects of nutrigenomics applications should be addressed proactively, as this new science develops and increasingly coalesces with other applications of genomics in medicine and public health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".