MRI‐Measured Bone Marrow Adipose Tissue is Strongly Negatively Associated With DXA‐Measured Bone Mineral
Bibliographic record
Abstract
Recent studies suggest that bone marrow adipose tissue (BMAT) might play a role in the pathogenesis of osteoporosis. Previous research using regional magnetic resonance spectroscopy (MRS) methods to measure BMAT reported inconsistent findings on the relationship between BMAT and Dual‐Energy Absorptiometry (DXA) ‐measured bone mineral density (BMD). In the present study we evaluated 52 healthy women (age 18–88 yrs, mean±SD, 48.4 ± 17.7 yrs; BMI, 24.5 ± 4.6 kg/m 2 ) with T1‐weighted whole‐body MRI‐measured total body adipose tissue (TBAT) and pelvic BMAT using conventional image segmentation methods (sliceOmatic 4.2, Tomovision Inc., Montreal). Total body and regional BMD was measured by whole body DXA (GE Lunar DPX, software version 4.7). A high correlation was observed between pelvic BMAT and BMD (total body BMD r = −0.723, p < 0.001; pelvic BMD r = −0.627, p < 0.001). The association between BMAT and BMD remained strong even after adjusting for age, BMI, and TBAT (total body BMD r =‐0.498, p < 0.001; pelvic BMD r= −0.449, p < 0.001). Pelvic BMAT was also highly correlated with age (r = 0.723, p < 0.001) but not with TBAT (r = −0.206, p = 0.15). Conclusions: MRI‐measured BMAT is strongly correlated with DXA‐measured BMD; and additional studies are needed to establish the extent to which these observations represent biological relations or DXA measurement artifacts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".