Dietary protein and skeletal health: a review of recent human research
Bibliographic record
Abstract
PURPOSE OF REVIEW: Both dietary calcium and vitamin D are undoubtedly beneficial to skeletal health. In contrast, despite intense investigation, the impact of dietary protein on calcium metabolism and bone balance remains controversial. A widely held view is that high intakes of animal protein result in increased bone resorption, reduced bone mineral density, and increased fractures because of its ability to generate a high fixed metabolic acid load. The purpose of this review is to present the recent or most important epidemiological and clinical trials in humans that evaluated dietary protein's impact on skeletal health. RECENT FINDINGS: Many epidemiological studies have found a significant positive relationship between protein intake and bone mass or density. Similarly, isotopic studies in humans have also demonstrated greater calcium retention and absorption by individuals consuming high-protein diets, particularly when the calcium content of the diet was limiting. High-protein intake may positively impact bone health by several mechanisms, including calcium absorption, stimulation of the secretion of insulin-like growth factor-1, and enhancement of lean body mass. The concept that an increase in dietary protein induces a large enough shift in systemic pH to increase osteoclastic bone resorption seems untenable. SUMMARY: Recent epidemiological, isotopic and meta-analysis studies suggest that dietary protein works synergistically with calcium to improve calcium retention and bone metabolism. The recommendation to intentionally restrict dietary protein to improve bone health is unwarranted, and potentially even dangerous to those individuals who consume inadequate protein.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".