Bone Marrow and Muscle Fat Infiltration Are Correlated among Postmenopausal Women With Osteoporosis: The AMBERS Cohort Study
Bibliographic record
Abstract
ABSTRACT Bone and muscle have shown to interact, but little is known about fat within bone and muscle. Clinical studies have isolated fat within bone and muscle using MRI. In this cross-sectional study, we hypothesized that bone marrow adiposity and muscle adiposity are related and that this relationship is associated with osteoporosis. Postmenopausal women aged 60 to 85 years were recruited as part of the Appendicular Muscle and Bone Extension Research Study (AMBERS). Participants completed dual-energy X-ray absorptiometry (DXA) of the hip and spine to diagnose osteoporosis. Muscle adiposity was measured with MRI at the 66% site of the leg. Fat segmentation was achieved using a semi-automated iterative threshold-optimizing algorithm (error < 5%). Peripheral quantitative computed tomography measured marrow density of the 4% distal tibia (surrogate for marrow fat) by threshold-based, edge-detection segmentations and by examining residuals from trabecular bone density regressed on trabecular tissue mineral density. Muscle adiposity from MRI was regressed on marrow density using linear regression. Models were further examined with an interaction with osteoporosis status. Among 312 women (aged 75.4 ± 5.9 years, body mass index [BMI] 29.5 ± 5.7 kg/m2), a larger amount of muscle fat was associated with lower marrow density at the 66% mid-tibia (B = 84.08 [27.56], p = 0.002) and at the 4% distal tibia (B = 129.17 [55.96], p = 0.022) after accounting for age, height, weight, average daily energy expenditure, hypertension, and diabetes. Interactions of this relationship with osteoporosis status were also significant. Upon probing these interactions, the relationships were significant only in women with osteoporosis but not in those without osteoporosis. Fat from bone marrow and muscle may be related to one another through the same phenomenon, which is likely also responsible for osteoporosis, but independent of hypertension and diabetes. More research should focus on the potential abnormalities in muscle and bone fat metabolism and mesenchymal cell commitment to fat within patients with osteoporosis. © 2019 American Society for Bone and Mineral Research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".