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A Maternal Diet Enriched with Longer Chain Omega‐ 3 Polyunsaturated Fatty Acids Increased Lipogenesis and Fetal Outcome in C57bl/6 Mice

2017· article· en· W4389018979 on OpenAlexaffabout
Olatunji Anthony Akerele, Sukhinder Kaur Cheema

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPregnancyFetusPolyunsaturated fatty acidLipogenesisInternal medicineEndocrinologyFatty acidBiologyFish oilFatty acid synthaseGestationLipid metabolismMedicineBiochemistry

Abstract

fetched live from OpenAlex

Nutritional strategy employed during pregnancy has a significant impact on pregnancy progression and outcome. However, there is paucity of evidence on the effect of omega (n)‐3 polyunsaturated fatty acids (PUFA) intake on maternal metabolic regulation and fetal outcome at different stages of pregnancy. We investigated the effects of diets varying in both the quantity and the quality of fat on maternal metabolic profile during pregnancy and its effect on the fetal number of C57BL/6 mice. Female mice (8 weeks) were fed diets varying in the quantity of fat (5% vs. 11%) for one week prior to mating and throughout pregnancy. The 5% diet contained fish oil as a source of n‐3 PUFA, while the 11% diet contained soybean oil. The females were checked for vaginal plug to confirm pregnancy. Maternal blood and livers were collected at early‐ (day 6.5), mid‐ (day 12.5) and late‐pregnancy (day 18.5). Lipids were extracted from red blood cells (RBC) and liver, and fatty acid composition was determined using gas chromatography. Plasma and liver triacylglycerol (TG) and total cholesterol (TC) were measured using kits. Hepatic mRNA expression of lipogenic enzymes (acetyl‐CoA carboxylase; ACC1 and fatty acid synthase; FAS) was measured using qPCR. Macrophage cholesterol efflux assay was performed using J774 cells treated with 2% maternal plasma. Plasma progesterone and estradiol were determined using Architect System. Fetal weight, implantation sites and the number of fetuses sustained throughout pregnancy were recorded. Two way analysis of variance was used to determine the main effects of diet and time, and Bonferrroni pairwise comparison was used to determine statistical difference when an interaction was observed; P<0.05 was considered significant. The 5% diet group had higher amount of RBC alpha‐linolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) at each stage of pregnancy, compared to the 11% group (P<0.0001). Interestingly, ALA was not detected at day 12.5 and 18.5 in the 11% group. The 5% group showed higher plasma TG (P<0.001) and lower TC (P<0.05), compared to the 11% group at mid and late pregnancy. Hepatic TC levels were higher in the 5% group, compared to the 11% group (P<0.05) at mid and late pregnancy. The plasma from the 5% group at mid and late pregnancy increased cholesterol efflux, compared to the 11% group (P<0.005). The mRNA expression of ACC1 and FAS were higher in the 5% group, compared to the 11% group (P<0.05) at mid and late pregnancy. Diet had an independent effect on maternal sex‐hormones, revealing significantly higher levels of progesterone and estradiol in the 5% group (P<0.005), compared to the 11% group. The number of fetuses sustained till day 18.5 was higher in the 5% group (average of 8 fetuses), compared to the 11% group (average of 5.1 fetuses) (P<0.05). In conclusion, a low fat diet causes an increase in de‐novo lipid synthesis, likely to meet the needs of the developing fetus. The 11% group showed rapid conversion of ALA to DHA at mid and late gestation. Higher levels of progesterone and estradiol in the 5% group may be responsible for sustaining a higher fetal number. Our findings highlight the importance of the quantity and the quality of fat during pregnancy on fetal outcome. Support or Funding Information Funded by Natural Sciences and Engineering Research Council of Canada (NSERC)

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.027
GPT teacher head0.286
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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