Diets Higher in Dairy Foods and Dietary Protein Support Bone Health during Diet- and Exercise-Induced Weight Loss in Overweight and Obese Premenopausal Women
Bibliographic record
Abstract
CONTEXT: Consolidation and maintenance of peak bone mass in young adulthood may be compromised by inactivity, low dietary calcium, and diet-induced weight loss. OBJECTIVE: We aimed to determine whether higher intakes of dairy foods, dietary calcium, and protein during diet- and exercise-induced weight loss affected markers of bone health. PARTICIPANTS: Participants included premenopausal overweight and obese women. DESIGN/INTERVENTION: Ninety participants were randomized into three groups (n = 30 per group): high protein and high dairy (HPHD), adequate protein and medium dairy (APMD), and adequate protein and low dairy (APLD), differing in dietary protein (30, 15, or 15% of energy, respectively), dairy foods (15, 7.5, or <2% of energy from protein, respectively), and dietary calcium (∼1600, ∼1000, or <500 mg/d, respectively). OUTCOME MEASURES: Serum and urine bone turnover biomarkers, serum osteoprotegerin (OPG), receptor activator of nuclear factor-κB ligand (RANKL), PTH, 25-hydroxyvitamin D, leptin, and adiponectin measured at 0 and 16 wk. RESULTS: All groups lost equivalent body weight (P < 0.05). N-telopeptide, C-telopeptide (CTX), urinary deoxypyridinoline, and osteocalcin increased in APLD (P < 0.01), whereas in HPHD, osteocalcin and procollagen 1 amino-terminal propeptide (P1NP) increased (P < 0.05), and all resorption markers remained unchanged. P1NP to CTX and OPG to RANKL ratios increased in HPHD (P < 0.005), and P1NP to CTX ratio decreased in APLD (P < 0.05). PTH decreased in HPHD and APMD vs. APLD (P < 0.005), and 25-hydroxyvitamin D increased in HPHD (P < 0.05), remained unchanged in APMD, and decreased in APLD (P < 0.05). Leptin decreased and adiponectin increased in APMD and HPHD only (P < 0.001). CONCLUSIONS: Hypoenergetic diets higher in dairy foods, dietary calcium, and protein with daily exercise, favorably affected important bone health biomarkers vs. diets with less of these bone-supporting nutrients.
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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 | 0.000 | 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".