Body mass index (BMI) and serum parathyroid hormone (sPTH) influence cortical bone in premenopausal women
Bibliographic record
Abstract
Continuous endogenous sPTH excess has been shown to have catabolic effects on trabecular and cortical bone in normal‐weight women. In this study we examined the relationship between sPTH levels and BMI on cortical and trabecular volumetric bone mineral density (vBMD) and geometry in 87 normocalcemic obese and non‐obese women. Measurements were made by peripheral quantitative computed tomography at 4% and 38% from the distal tibia. In 43 premenopausal women, we used the median to categorize sPTH (pg/mL) as higher (70 ± 24) and lower (29 ± 4), and categorized BMI (kg/m2) into obese (39 ± 9) and non‐obese (26.5 ± 1.8). A similar analysis was performed for a postmenopausal population. Not surprisingly, we found that obesity increased cortical vBMD and content (BMC), geometric properties (area, thickness, circumference), polar moment of inertia and the stress:strain index in all women (P < 0.05), and also increased trabecular vBMD and BMC (P < 0.02) in the premenopausal women. We found an interaction (2‐way ANOVA) between BMI and sPTH for cortical BMC and geometric variables (p < 0.05) in the premenopausal women. Hence, the positive influence of BMI on cortical bone geometric properties is dependent on higher sPTH in the younger women. These findings suggest that the altered hormonal milieu associated with obesity is an important determinant in modulating the effects of excess weight on the skeleton.
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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 | 0.001 | 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".