Effects of Pregnancy and Lactation on Bone Microstructure and Material Properties in a Rat Model of Bariatric Surgery
Bibliographic record
Abstract
Obesity is a major public health issue worldwide. Despite various approaches to weight loss, the most effective technique for reducing obesity, as well as diabetes and associated diseases, is bariatric surgery. Increasingly, young women without children are undergoing bariatric surgery, vertical sleeve gastrectomy (VSG) being the most common procedure nowadays. However, despite several reports suggesting bone loss after VSG, little is known about the potential additive effects of gestation and lactation after VSG to bone health. This study investigated the combined effects of pre-gestational VSG and subsequent gestation/lactation on bone metabolism in a rat model fed a high fat high sugar (HFHS) diet, with a focus on bone biomechanics, mass, microarchitecture and material properties. Furthermore, bone mass and remodelling were followed longitudinally by microCT prior to surgery, 4 weeks post-surgery, after weaning and at sacrifice. Significant alterations in bone mass and microarchitecture, characterized by changes in trabecular thickness and number, as well as changes in bone formation and resorption were influenced by both surgery and reproductive demands. Mechanical testing at sacrifice demonstrated compromised long bone fragility, in rat with HFHS regardless of the surgical procedure (Sham or VSG). Furthermore, analysis of bone material properties highlighted potential disruptions in the pattern of bone mineralization in sham and VSG animals fed a HFHS diet. These findings underscore the complex interplay between pre-gestational VSG and subsequent gestation/lactation in modulating bone metabolism in the investigated rat model. The preclinical rat model may help with optimizing surgical strategies and developing targeted interventions to mitigate potential bone-related complications associated with VSG in reproductive-aged individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".