Recent advances and current controversies in genetic testing for personalized nutrition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Considerable interest in personalized nutrition exists among the general public, policymakers, healthcare organizations and the private sector, but there is also skepticism of its utility. The present review aims to provide a summary of current controversies in the field of nutrigenomics, and to highlight recent research on the potential impact of implementing genetic testing for personalized nutrition in practice. RECENT FINDINGS: Numerous companies already offer genetic testing for personalized nutrition based on research developments in nutritional genomics. However, controversy exists over whethexr genetics contributes to interindividual responses to diet; the utility of single genetic variants versus genetic risk scores; the ability of DNA-based nutritional advice to elicit positive behavior change and health effects; and whether genetic information makes a difference on the type of dietary advice provided. Potential factors contributing to the discrepant viewpoints are discussed. SUMMARY: Despite the existing controversies, a solid body of evidence demonstrates that genetic testing for personalized nutrition is a powerful tool to guide dietary recommendations to improve health and performance, and to elicit positive behavior change.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".