Serum markers of bone turnover are increased by modest weight loss with or without weight-bearing exercise in overweight premenopausal women
Bibliographic record
Abstract
Weight loss improves metabolic fitness and reduces morbidity and mortality; however, weight reduction also reduces bone mineral density (BMD) and increases bone turnover. Weight-bearing aerobic exercise may preserve bone mass and maintain normal bone turnover during weight reduction. We investigated the impact of weight-bearing and nonweight-bearing exercise on serum markers of bone formation and breakdown during short-term, modest weight loss in overweight premenopausal women. Subjects (n = 36) were assigned to 1 of 3 weight-loss interventions designed to produce a 5% reduction in body weight over 6 weeks: (i) energy restriction only (n = 11; DIET); (ii) energy restriction plus nonweight-bearing exercise (n = 12, CYCLE); or (iii) energy restriction plus weight-bearing exercise (n = 13, RUN). Bone turnover markers were measured in serum collected at baseline and after weight loss. All groups achieved a ~5% reduction in body weight (DIET = 5.2%; CYCLE = 5.0%; RUN = 4.7%). Osteocalcin (OC) and C-terminal telopeptide of type I collagen (CTX) increased with weight loss in all 3 groups (p < 0.05), whereas bone alkaline phosphatase was unaltered by the weight-loss interventions. At baseline, OC and CTX were positively correlated (r = 0.36, p = 0.03), but the strength of this association was diminished (r = 0.30, p = 0.06) after weight loss. Modest weight loss, regardless of method, resulted in a significant increase in both OC and CTX. Low-impact, weight-bearing exercise had no effect on serum markers of bone formation or resorption in premenopausal women during weight loss. Future studies that examine the effects of high-impact, weight-bearing activity on bone turnover and BMD during weight loss are warranted.
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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 | 0.001 |
| 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".