Maternal high-fat-diet exposure is associated with elevated blood pressure and sustained increased leptin levels through epigenetic memory in offspring
Bibliographic record
Abstract
Maternal metabolism dysregulation during pregnancy predisposes offspring to major diseases, including hypertension, in later life, but the mechanism involved remains to be fully elucidated. A high-fat-diet (HFD) pregnant rat model was used to investigate whether excessive intrauterine lipid exposure was associated with elevated blood pressure in offspring and increased levels of leptin, an important biomarker and mediator of vascular dysfunction and hypertension. We found that gestational hyperlipidemia predisposed offspring to blood pressure elevation and sustained increases in leptin levels with no difference in body weight in the rat model. Increased leptin expression and leptin promoter hypomethylation were found in adipose tissues of HFD-exposed offspring. The treatment of mesenchymal stem cells with free fatty acids during adipogenic differentiation resulted in increased leptin expression, accompanied by leptin promoter hypomethylation. In addition, we also followed up 121 children to evaluate the association between maternal triglyceride levels and offspring blood pressure. Consistent with the animal study results, we observed elevated serum leptin levels and blood pressure in the offspring born to women with gestational hypertriglyceridemia. Our findings provide new insights that maternal hyperlipidemia is associated with elevated blood pressure in offspring and is associated with increases in leptin levels through epigenetic memory.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".