Serum measures of docosahexaenoic acid (DHA) synthesis underestimates whole body DHA synthesis in male and female mice
Bibliographic record
Abstract
Females have higher docosahexaenoic acid (DHA) levels than males, proposed to be a result of higher DHA synthesis rates from α-linolenic acid (ALA). However, DHA synthesis rates are reported to be low, and have not been directly compared between sexes. Here, we apply a new compound specific isotope analysis model to determine n-3 PUFA synthesis rates in male and female mice and assess its potential translation to human populations. Male and female C57BL/6N mice were allocated to one of three 12-week dietary interventions with added ALA, eicosapentaenoic acid (EPA) or DHA. The diets included low carbon-13 (δ13C)-n-3 PUFA for 4 weeks, followed by high δ13C-n-3 PUFA for 8 weeks (n=4 per diet, time point, sex). Following the diet switch, blood and tissues were collected at multiple time points, and fatty acid levels and δ13C were determined and fit to one-phase exponential decay modeling. Hepatic DHA synthesis rates were not different (p>0.05) between sexes. However, n-3 docosapentaenoic acid (DPAn-3) synthesis from dietary EPA was 66% higher (p<0.05) in males compared to females, suggesting higher synthesis downstream of DPAn-3 of females. Estimates of percent conversion of dietary ALA to serum DHA was 0.2%, in line with previous rodent and human estimates, but severely underestimates percent dietary ALA conversion to whole body DHA of 9.5%. Taken together, our data indicates that reports of low human DHA synthesis rates may be inaccurate, with synthesis being much higher than previously believed. Future animal studies and translation of this model to humans are needed for greater understanding of n-3 PUFA synthesis and metabolism, and whether the higher-than-expected ALA-derived DHA can offset dietary DHA recommendations set by health agencies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".