Effects of weight-loss interventions on bone health in people living with obesity
Bibliographic record
Abstract
Strategies to reduce weight in people living with obesity (PwO) include calorie restriction, metabolic and bariatric surgery (MBS), and anti-obesity drugs, including glucagon-like peptide-1 receptor agonists (GLP-1Ra). Although weight loss in PwO has many health benefits, it can result in increased bone loss and fracture risk. Indeed, the consequences of weight loss interventions are well known: (1) significant weight loss induced by caloric restriction and MBS results in high turnover bone loss and (2) unlike calorie restriction, PwO experience a substantial deterioration in bone microarchitecture and strength associated with an increased risk of fracture after MBS, especially malabsorptive procedures. GLP-1 may enhance bone metabolism and improve bone quality, and liraglutide appears to have a positive effect on bone health despite significant weight loss in several rodent models. However, most of the positive effects on bone have been observed at concentrations much higher than those approved for obesity care in humans. The effects of GLP-1Ra on bone health in PwO are still limited; however, significant weight loss induced by GLP-1Ra may also result in accelerated bone turnover and bone loss, and semaglutide could lead to an increased risk of fractures in the at-risk population. The mechanisms responsible for the adverse skeletal effects of MBS are not yet fully understood, and there are insufficient human studies supporting pathophysiological hypotheses. However, data suggest that multiple mechanisms are involved, including nutritional factors, mechanical unloading, hormonal factors, adipokines, and alterations in the gut microbiome. Recommendations for the prevention and treatment of osteoporosis secondary to MBS are now available, and the efficacy of anti-osteoporosis medications in preventing bone loss has been evaluated in two randomized controlled trials. Priorities for future research include the development of effective approaches to reduce fracture risk in PwO following MBS and investigation of the effects of anti-obesity drugs on bone health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".