Effects of high-protein intake on bone turnover in long-term bed rest in women
Bibliographic record
Abstract
Bed rest (BR) causes bone loss, even in otherwise healthy subjects. Several studies suggest that ambulatory subjects may benefit from high-protein intake to stimulate protein synthesis and to maintain muscle mass. However, increasing protein intake above the recommended daily intake without adequate calcium and potassium intake may increase bone resorption. We hypothesized that a regimen of high-protein intake (HiPROT), applied in an isocaloric manner during BR, with calcium and potassium intake meeting recommended values, would prevent any effect of BR on bone turnover. After a 20-day ambulatory adaptation to a controlled environment, 16 women participated in a 60-day, 6° head-down-tilt (HDT) BR and were assigned randomly to 1 of 2 groups. Control (CON) subjects (n = 8) received 1 g/(kg body mass·day)−1 dietary protein. HiPROT subjects (n = 8) received 1.45 g protein/(kg body mass·day)−1 plus an additional 0.72 g branched-chain amino acids per day during BR. All subjects received an individually tailored diet (before HDTBR: 1888 ± 98 kcal/day; during HDTBR: 1604 ± 125 kcal/day; after HDTBR: 1900 ± 262 kcal/day), with the CON group’s diet being higher in fat and carbohydrate intake. High-protein intake exacerbated the BR-induced increase in bone resorption marker C-telopeptide (>30%) (p < 0.001) by the end of BR. Bone formation markers were unaffected by BR and high-protein intake. We conclude that high-protein intake in BR might increase bone loss. Further long-duration studies are mandatory to show how the positive effect of protein on muscle mass can be maintained without the risk of reducing bone mineral density.
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 | 0.000 |
| 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".