Heterogeneity in the response to n-3 polyunsaturated fatty acids
Bibliographic record
Abstract
PURPOSE OF REVIEW: A central goal in the study of long chain n-3 polyunsaturated fatty acids (PUFA) is to translate findings from the basic sciences to the population level to improve human health and prevent chronic diseases. A tenet of this vision is to think in terms of precision medicine and nutrition, that is, stratification of individuals into differing groups that will have different needs across the lifespan for n-3 PUFAs. Therefore, there is a critical need to identify the sources of heterogeneity in the human population in the dietary response to n-3 PUFA intervention. RECENT FINDINGS: We briefly review key sources of heterogeneity in the response to intake of long chain n-3 PUFAs. These include background diet, host genome, composition of the gut microbiome, and sex. We also discuss the need to integrate data from newer rodent models (e.g. population-based approaches), multi -omics, and analyses of big data using machine learning and data-driven cluster analyses. SUMMARY: Accounting for vast heterogeneity in the human population, particularly with the use of big data integrated with preclinical evidence, will drive the next generation of precision nutrition studies and randomized clinical trials with long-chain n-3 PUFAs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".