Adipose tissue and volumetric bone mineral density of older Afro-Caribbean men
Bibliographic record
Abstract
Although low body weight is a risk factor for osteoporosis-related fractures, conflicting data exist for the association between adiposity and bone mineral density (BMD). Studies examining these relationships have measured body fat and BMD with dual-energy X-ray absorptiometry (DXA), which cannot distinguish subcutaneous adipose tissue area (SAT) from total adiposity or trabecular from cortical bone. To investigate the relationship between adiposity and BMD further, we analyzed body composition and adipose tissue distribution by quantitative computed tomography (QCT) in 1829 Afro-Caribbean men aged 40 years and older from a population-based sample. Cortical volumetric BMD, muscle cross-sectional area, total adipose tissue area (TAT), and percentage SAT were measured at the proximal tibia. Trabecular volumetric BMD was measured at the distal tibia. We used analysis of covariance to test for associations between quartile of the adipose tissue measures and BMD, adjusting for anthropometric, health, and lifestyle factors. Higher TAT was associated with lower cortical BMD in both unadjusted and adjusted models (p < .001). Men with a higher percentage SAT had greater cortical BMD (p < .001). Similar associations were seen between percent SAT and trabecular BMD at the distal tibia. These results indicate that total adiposity is a potentially important correlate of bone mass in older men and that different fat depots may have opposing associations with bone mass. Additional research is needed to better understand the mechanisms underlying the relationship between body fat distribution and bone mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".