Abstract 034: Omega-3 Fatty Acid Biomarkers and Incident Type 2 Diabetes: An Individual Participant-level Pooling Project of 20 Prospective Cohort Studies
Bibliographic record
Abstract
Background: Effects of omega-3 fatty acids on the prevention of type 2 diabetes (T2D) are unclear. Relatively few prospective studies have utilized objective omega-3 biomarkers to assess risk. Aims: To assess the prospective relationship between circulating and tissue levels of alpha linoleic acid (ALA), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA), with respect to risk of T2D. Methods: A global consortium of 20 prospective cohort studies from 14 nations with assessments of ALA, EPA, DPA, or DHA in adults (age > 18 years) were identified through July 2017 and included in this investigation. A pre-specified analytic protocol, including definitions for exposures, covariate list, disease outcome definitions, and subgroup analyses was developed and followed in new participant-level cohort analysis. Associations were pooled using inverse variance-weighted meta-analysis. Results: Among 65,147 participants, 16,693 incident cases of T2D occurred during follow-up (median follow-up in the cohorts ranged from 2.5 to 21.2 years). In pooled multivariate analysis, per interquintile range (difference between the midpoints of the first and fifth quintile for each fatty acid), EPA, DPA, DHA, and their sum ( Figure 1 ) were associated with 8%, 21%, 18%, and 19% lower risk of T2D, respectively (all P <0.001). Higher levels of ALA were not significantly associated with T2D. Associations were consistent across different lipid compartments and pre-specified subgroups, including by age, sex, and geographic region, as well as in several sensitivity analyses. Conclusion: Higher circulating and tissue biomarkers of seafood-derived omega-3 fatty acids, EPA, DPA, and DHA, were associated with lower risk of T2D in a global consortium of prospective studies. Plant-derived ALA was not significantly associated with risk.
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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 | 0.064 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".