Areal and volumetric bone mineral density and geometry at two levels of protein intake during caloric restriction: A randomized, controlled trial
Bibliographic record
Abstract
Weight reduction induces bone loss by several factors, and the effect of higher protein (HP) intake during caloric restriction on bone mineral density (BMD) is not known. Previous study designs examining the longer-term effects of HP diets have not controlled for total calcium intake between groups and have not examined the relationship between bone and endocrine changes. In this randomized, controlled study, we examined how BMD (areal and volumetric), turnover markers, and hormones [insulin-like growth factor 1 (IGF-1), IGF-binding protein 3 (IGFBP-3), 25-hydroxyvitamin D, parathyroid hormone (PTH), and estradiol] respond to caloric restriction during a 1-year trial using two levels of protein intake. Forty-seven postmenopausal women (58.0 ± 4.4 years; body mass index of 32.1 ± 4.6 kg/m(2) ) completed the 1-year weight-loss trial and were on a higher (HP, 24%, n = 26) or normal protein (NP, 18%, n = 21) and fat intake (28%) with controlled calcium intake of 1.2 g/d. After 1 year, subjects lost 7.0% ± 4.5% of body weight, and protein intake was 86 and 60 g/d in the HP and NP groups, respectively. HP compared with NP diet attenuated loss of BMD at the ultradistal radius, lumbar spine, and total hip and trabecular volumetric BMD and bone mineral content of the tibia. This is consistent with the higher final values of IGF-1 and IGFBP-3 and lower bone-resorption marker (deoxypyridinoline) in the HP group than in the NP group (p < .05). These data show that a higher dietary protein during weight reduction increases serum IGF-1 and attenuates total and trabecular bone loss at certain sites in postmenopausal women.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".