Impact of maternal high saturated fat diet on bone lipid content in weanling and 3 month old female offspring
Bibliographic record
Abstract
High saturated fat (HSFA) diets in rodent models can have adverse effects on healthy bone development. Past studies have focused on bone development of the primary consumer (parent) and did not examine indirect (in utero programming) or direct (fat consumption through milk) diet‐mediated effects in offspring. Thus, the objective of this study was to determine if maternal consumption of HSFA diet influences bone lipid content in female rat offspring at weaning (19 days) and young adulthood (3 months). Female Wistar rats (28 days old) were fed control (CON; AIN93G, 7% soybean oil) or HSFA (HF; AIN93G, 20% lard) diet for 10 weeks, bred, and remained on the same diet throughout gestation and lactation. After weaning, female offspring from both treatments were fed CON. Femur lipids of mothers and their 19 day and 3 month old offspring were analyzed. After 16 weeks on HSFA, maternal femurs had 12 and 34% more saturates (SFA) and monoenes (MUFA), respectively, and 45% less polyenes (PUFA) compared to CON. Similar effects were seen with 19 day olds (2 and 35% less SFA and PUFA, respectively, and 51% higher MUFA). However, after 9 weeks of CON, 3 month olds from mothers fed HF became more similar to CON (3 and 11% lower MUFA and PUFA, respectively, and 14% higher SFA). These results suggest that maternal diet can influence offspring bone lipids and the effects are somewhat reversible by early adulthood. The project was funded by NSERC (PJL & WEW).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".