Specific Modulation of Vertebral Marrow Adipose Tissue by Physical Activity
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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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 | 0.000 |
| 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) | 0.001 | 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".