Specific Modulation of Vertebral Marrow Adipose Tissue by Physical Activity
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Bibliographic record
Abstract
ABSTRACT Marrow adipose tissue (MAT) accumulation with normal aging impacts the bone, hemopoiesis, and metabolic pathways. We investigated whether exercise was associated with lower MAT, as measured by vertebral marrow fat fraction (VFF) on magnetic resonance imaging. A total of 101 healthy individuals (54 females) aged 25 to 35 years without spine or bone disease but with distinct exercise histories were studied. Long-distance runners (67 km/wk, n = 25) exhibited lower mean lumbar VFF (27.9% [8.6%] versus 33.5% [6.0%]; p = 0.0048) than non-sporting referents (n = 24). In habitual joggers (28 km/wk, n = 30), mean lumbar VFF was 31.3% (9.0%) (p = 0.22 versus referents) and 6.0 percentage points lower than referents at vertebrae T10, T11, and T12 (p ≤ 0.023). High-volume road cycling (275 km/wk, n = 22) did not impact VFF. 3D accelerations corresponding to faster walking, slow jogging, and high-impact activities correlated with lower VFF, whereas low-impact activities and sedentary time correlated with higher mean lumbar VFF (all p ≤ 0.05). Given an estimated adipose bone marrow conversion of 7% per decade of life, long distance runners, with 5.6 percentage points lower VFF, showed an estimated 8-year younger vertebral marrow adipose tissue phenotype. Regression analysis showed a 0.7 percentage point reduction in mean lumbar VFF with every 9.4 km/wk run (p = 0.002). This study presents the first evidence in humans or animals that specific volumes and types of exercise may influence the age-determined adipose marrow conversion and result in low MAT. These results identify a potentially modifiable risk factor for prevalent chronic conditions related to bone metabolism, hemopoietic production, and other metabolic functions with potential global health applications. © 2017 American Society for Bone and Mineral Research.
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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 | 0.001 |
| 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 it