Retroconversion is a minor contributor to increases in eicosapentaenoic acid following docosahexaenoic acid feeding as determined by compound specific isotope analysis in rat liver
Bibliographic record
Abstract
Dietary docosahexaenoic acid (DHA, 22:6n-3) not only increases blood and tissue levels of DHA, but also eicosapentaenoic acid (EPA, 20:5n-3). It is generally believed that this increase is due to DHA retroconversion to EPA, however, a slower conversion of α-linolenic acid (ALA, 18:3n-3) derived EPA to downstream metabolic products (i.e. slower turnover of EPA) is equally plausible. In this study, 21-day old Long Evans rats were weaned onto an ALA only or DHA + ALA diet for 12 weeks. Afterwards, livers were collected and the natural abundance 13 C-enrichment was determined by compound specific isotope analysis (CSIA) of liver EPA by isotope ratio mass-spectrometry and compared to dietary ALA and DHA 13 C-enrichment. Isotopic signatures (per mil, ‰) for liver EPA were not different ( p > 0.05) between the ALA only diet (−25.89 ± 0.39 ‰, mean ± SEM) and the DHA + ALA diet (−26.26 ± 0.40 ‰), suggesting the relative contribution from dietary ALA and DHA to liver EPA did not change. However, with DHA feeding estimates of absolute EPA contribution from ALA increased 4.4-fold (147 ± 22 to 788 ± 153 nmol/g) compared to 3.2-fold from DHA (91 ± 14 to 382 ± 13 nmol/g), respectively. In conclusion, CSIA of liver EPA in rats following 12-weeks of dietary DHA suggests that retroconversion of DHA to EPA is a relatively small contributor to increases in EPA, and that this increase in EPA is largely coming from elongation/desaturation of ALA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".