High Maternal Adiposity During Pregnancy Programs an Imbalance in the Lipidome and Predisposes to Diet-induced Hepatosteatosis in the Offspring
Bibliographic record
Abstract
ABSTRACT Background Exposure to high maternal adiposity in utero is a significant risk factor for the later-life development of metabolic syndrome (MetS), including non-alcoholic fatty liver disease (NAFLD). We have previously shown that high pre-pregnancy adiposity programs adipose tissue dysfunction in the offspring, leading to spillover of fatty acids into the circulation, a key pathogenic event in obesity-associated MetS. Herein, we hypothesized that programming of adipose tissue dysfunction in offspring born to overweight dams increases the risk for developing NAFLD. Results Females heterozygous for leptin receptor deficiency (Het db ) were used as a model of high pre-pregnancy adiposity. Wild-type (Wt) offspring born to Het db pregnancies gained significantly more body fat following high fat/fructose diet (HFFD) compared to Wt offspring born to Wt dams. HFFD increased circulating free fatty acids (FFA) in male offspring of control dams, while FFA levels were similar in HFFD-fed offspring from Wt dams compared to CD or HFFD-Wt offspring from Het db dams. Despite female-specific protection from diet-induced FFA spillover, both male and female offspring from Het db . dams were more susceptible to diet-induced hepatosteatosis. Lipidomic analysis revealed that CD-offspring of overweight dams had decreased hepatic PUFA levels compared to control offspring. Changes to saturated fatty acids (SFA) and the de novo lipogenic (DNL) index were diet driven; however, there was a significant effect of the intrauterine environment on FA elongation and Δ9 desaturase activity. Conclusion High maternal adiposity during pregnancy programs a susceptibility to diet-induced hepatosteatosis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".