Meta-Analysis of the Effect of the Acid-Ash Hypothesis of Osteoporosis on Calcium Balance
Bibliographic record
Abstract
The acid-ash hypothesis posits that protein and grain foods, with a low potassium intake, produce a diet acid load, net acid excretion (NAE), increased urine calcium, and release of calcium from the skeleton, leading to osteoporosis. The objectives of this meta-analysis were to assess the effect of changes in NAE, by manipulation of healthy adult subjects' acid-base intakes, on urine calcium, calcium balance, and a marker of bone metabolism, N-telopeptides. This meta-analysis was limited to studies that used superior methodological quality for the study of calcium metabolism. We systematically searched the literature and included studies if subjects were randomized to the interventions and followed the recommendations of the Institute of Medicine's Panel on Calcium and Related Nutrients for calcium studies. Five of 16 studies met the inclusion criteria. The studies altered the amount and/or type of protein. Despite a significant linear relationship between an increase in NAE and urinary calcium (p < 0.0001), there was no relationship between a change of NAE and a change of calcium balance (p = 0.38; power = 94%). There was no relationship between a change of NAE and a change in the marker of bone metabolism, N-telopeptides (p = 0.95). In conclusion, this meta-analysis does not support the concept that the calciuria associated with higher NAE reflects a net loss of whole body calcium. There is no evidence from superior quality balance studies that increasing the diet acid load promotes skeletal bone mineral loss or osteoporosis. Changes of urine calcium do not accurately represent calcium balance. Promotion of the "alkaline diet" to prevent calcium loss is not justified.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".