A high protein diet reduces whole body fat mass in healthy mature female rats, but does not affect whole body bone mineral density
Bibliographic record
Abstract
High protein diets are hypothesized to reduce body weight, but affect bone mass adversely due to higher acid load and thereby mineral excretion. The objective was to determine if high protein diets in line with the acceptable macronutrient distribution range for protein would effect body weight, body composition and bone mass longitudinally over 4 to 24 mo. This report captures data from 4 and 8 mo of study. Mature (70 d of age) female Sprague Dawley rats (n=10/group/age) were randomized to either a mixed protein diet with 15% of energy as protein to reflect North American dietary protein intakes (NP) or a diet made with higher protein (HP) at 35% of energy. Diets were balanced in energy, fat, Ca, P, Mg and Zn and fed ad libitum. Measurements included body weight, feed intake plus body composition and bone mineral density (BMD). Differences between groups were examined using factorial ANOVA (diet, age and interaction effects); no interaction effects were observed. Consumption of a high protein diet at 35% of energy from mixed plant and animal sources while ensuring a balanced intake of minerals leads to enhanced lean mass with reduced fat mass and no adverse affects on whole body BMD noted. Funded by CIHR.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".