Evaluating Changes in Omega-3 Fatty Acid Intake after Receiving Personal FADS1 Genetic Information: A Randomized Nutrigenetic Intervention
Bibliographic record
Abstract
Nutrigenetics research is anticipated to lay the foundation for personalized dietary recommendations; however, it remains unclear if providing individuals with their personal genetic information changes dietary behaviors. Our objective was to evaluate if providing information for a common variant in the fatty acid desaturase 1 (FADS1) gene changed omega-3 fatty acid (FA) intake and blood levels in young female adults (18–25 years). Participants were randomized into Genetic (intervention) and Non-Genetic (control) groups, with measurements taken at Baseline and Final (12 weeks). Dietary intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) was assessed using an omega-3 food frequency questionnaire. Red blood cell (RBC) FA content was quantified by gas chromatography. Implications of participation in a nutrigenetics study and awareness of omega-3 FAs were assessed with online questionnaires. Upon completion of the study, EPA and DHA intake increased significantly (p = 1.0 × 10−4) in all participants. This change was reflected by small increases in RBC %EPA. Participants in the Genetic group showed increased awareness of omega-3 terminology by the end of the study, reported that the dietary recommendations were more useful, and rated cost as a barrier to omega-3 consumption less often than those in the Non-Genetic group. Providing participants FADS1 genetic information did not appear to influence omega-3 intake during the 12 weeks, but did change perceptions and behaviors related to omega-3 FAs in this timeframe.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".