Global Comparison of Erythrocyte EPA and DHA Concentrations in Pregnant Women
Bibliographic record
Abstract
BACKGROUND: Adequate concentrations of long-chain omega-3 (LC n-3) PUFAs, specifically EPA and DHA, are critical for maternal health and fetal development during pregnancy. Despite their importance, global data on maternal blood concentrations of EPA+DHA remain sparse and inconsistent, partially due to differences in measurement methodologies. OBJECTIVES: This study assessed global maternal blood concentrations of EPA+DHA during pregnancy by synthesizing data from observational studies and RCTs from the last 20 y (2004-2025). METHODS: Non-red blood cell (RBC) based EPA+DHA blood concentrations from published studies were standardized using conversion equations to estimate relative EPA+DHA percentages in RBCs [estimated Omega-3 Index (eO3I)]. Country mean eO3I levels were classified into 4 categories based on literature-defined thresholds. RESULTS: An analysis of 66 studies involving 33,390 pregnant women from 28 countries revealed significant geographical disparities in eO3I levels. Only the Seychelles, Norway, and Ghana achieved desirable levels (>8%). Some Asian countries (Japan, Taiwan, and Singapore), Malawi, Tanzania, and Northern European nations (Belgium, Netherlands, Iceland, Denmark, and Sweden) exhibited sufficient/moderate levels (>6%-8%). Most countries, including the United States, Canada, Mexico, Brazil, Chile, Australia, the United Kingdom, Germany, Switzerland, Italy, Croatia, and Spain, demonstrated insufficient/low levels (>4%-6%). Meanwhile, China, India, and Iran showed very low/undesirable levels (≤4%). CONCLUSIONS: These findings highlight widespread insufficiency in maternal EPA+DHA status globally, with particularly severe deficiencies observed in Asia and parts of Europe. This study underscores the need for more research to ultimately define the optimal EPA+DHA concentrations during pregnancy using standardized blood biomarkers, along with pregnancy-specific reference ranges, to facilitate targeted nutritional strategies aimed at optimizing health outcomes for both mother and child. Future studies should focus on addressing data gaps, refining intake recommendations, and promoting accessible supplementation strategies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".