The Impact of Maternal Body Composition and Dietary Fat Consumption upon Placental Lipid Processing and Offspring Metabolic Health
Bibliographic record
Abstract
The proportion of women of reproductive age who are overweight or obese is increasing globally. Gestational obesity is strongly associated in both human studies and animal models with early-onset development of adult-associated metabolic diseases including metabolic syndrome in the exposed offspring. However, animal model studies have suggested that gestational diet in obese pregnancies is an independent but underappreciated mediator of offspring risk for later life metabolic disease, and human diet consumption data have highlighted that many women do not follow nutritional guidelines prior to and during pregnancy. Thus, this review will highlight how maternal diet independent from maternal body composition impacts the risk for later-life metabolic disease in obesity-exposed offspring. A poor maternal diet, in combination with the obese metabolic state, are understood to facilitate pathological in utero programming, specifically through changes in lipid handling processes in the villous trophoblast layer of the placenta that promote an environment associated with the development of metabolic disease in the offspring. This review will additionally highlight how maternal obesity modulates villous trophoblast lipid processing functions including fatty acid transport, esterification and beta-oxidation. Further, this review will discuss how altering maternal gestational diet may ameliorate these functional changes in lipid metabolic processes in the obese placenta.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".