High Folic Acid Intake during Pregnancy Lowers Body Weight and Reduces Femoral Area and Strength in Female Rat Offspring
Bibliographic record
Abstract
Rats fed gestational diets high in multivitamin or folate produce offspring of altered phenotypes. We hypothesized that female rat offspring born to dams fed a gestational diet high in folic acid (HFol) have compromised bone health and that feeding the offspring the same HFol diet attenuates these effects. Pregnant rats were fed diets with either recommended folic acid (RFol) or 10-fold higher folic acid (HFol) amounts. Female offspring were weaned to either the RFol or HFol diet for 17 weeks. HFol maternal diet resulted in lower offspring body weights (6%, P = 0.03) and, after adjusting for body weight and femoral length, smaller femoral area (2%, P = 0.03), compared to control diet. After adjustments, HFol pup diet resulted in lower mineral content (7%, P = 0.01) and density (4%, P = 0.002) of lumbar vertebra 4 without differences in strength. An interaction between folate content of the dam and pup diets revealed that a mismatch resulted in lower femoral peak load strength (P = 0.01) and stiffness (P = 0.002). However, the match in folate content failed to prevent lower weight gain. In conclusion, HFol diets fed to rat dams and their offspring affect area and strength of femurs and mineral quantity but not strength of lumbar vertebrae in the offspring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".