A317 THE EFFECT OF OMEGA-3 PUFA ON THE DEVELOPING MICROBIOTA AND IMMUNITY OF INFANTS
Bibliographic record
Abstract
It is currently unknown how maternal supplementation with fish oils during gestation or lactation affects the microbiome-immune nexus in their infants. Our lab has previously shown that feeding in pregnant mice alters the gut microbiome, blocks inflammation, and increases mortality in the offspring during gut infection. In a recently published meta-analysis, we report that fish oil supplements in humans may adversely affect immune development in infants. Taken together, these evidences indicate that maternal exposure to fish oil supplements impairs gut immunity in growing infants. We then furthered these findings with a prospective clinical study. The aim of this study is to determine how maternal exposure to fish oil supplements affect offspring’s gut microbiome and immunity. 109 pregnant or lactating women who either had or had not supplemented their diets with fish oil, rich in omega-3 polyunsaturated fatty acids (PUFA), were recruited. To participate in our trial, the infants of these women had to be healthy, full term infants born in the Okanagan valley. The women in the supplemented group were directed to consume fish oil supplements until their infants were 6 month of age, whereas the women in the control group did not take any fish oil supplements during this time. We collected the meconium and stool from the infants at 1 week of age, and then again every month for 6 months. Concurrently, we collected breastmilk samples from the mothers for fatty acid analysis to ensure compliance with our instructions. To determine how maternal fish oil supplements altered the offspring’s gut microbiome, we compared the fecal bacteria composition of the infants over time using Illumina MiSeq. We analyzed the breastmilk samples for lipid contents and immune markers and found that women supplementing with fish oil had significantly greater EPA, but not DHA, in their breastmilk. We also found that the supplementing women had less IgA in their breastmilk compared to their non-fish oil counterparts as well as a trend towards higher anti-inflammatory and less pro-inflammatory cytokines suggesting fish oil decreased immune responsiveness. Our results suggest that maternal fish oil supplements change the composition of the mother’s breastmilk, which in turn, alters their infant’s microbiome. This has implications on the current recommendations for infant health. CAG, CIHR
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".