A randomized controlled trial of genetic information on dietary intake
Bibliographic record
Abstract
Personal genetic information has become obtainable as a result of commercial genetic tests, but its effect on dietary change is not clear. We compared the effects of providing DNA‐based dietary advice with general recommendations on dietary intake using a randomized trial. Subjects aged 20–35 years (n=102) completed a baseline food frequency questionnaire (FFQ) and were randomized to an intervention (I) or control (C) group. Subjects in the intervention group were genotyped for the angiotensin‐I converting enzyme ( ACE ) gene, which has been associated with salt‐sensitive hypertension, and were given personalized advice for sodium intake based on their ACE genotype. The control group was given a general recommendation for sodium intake without genetic information. A second FFQ was collected 3 months later. A significant reduction in sodium intake was observed in subjects in the intervention group who were informed that they carried the risk allele for ACE when compared to the control group (−174.9±89.1 mg/day [I] vs. 225.5±123.5 mg/day [C]; p = 0.008). No difference was observed between subjects who were informed that they did not carry the risk allele and controls (111.2±145.0 mg/day [I] vs. 225.5±123.5 mg/day [C]; p = 0.48). The results suggest that personalized advice based on genetic information may be superior to general advice in motivating individuals to adopt dietary changes. Grant Funding Source : Advanced Foods and Materials Network
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".