Marrow adipose tissue gradient is preserved through high protein diet and bed rest. A randomized crossover study
Bibliographic record
Abstract
CONTEXT: Marrow adipose tissue (MAT) has a peripheral to central distribution in adults, higher in peripheral bones. Similarly, the spine has a caudal to cephalad MAT distribution, higher in lumbar vertebras. Diet and the level of physical activities are known modulators of MAT with significant impact on bone; however, whether these can modulate the MAT gradient is unknown. OBJECTIVE: To measure the effect of high protein diet and bed rest interventions on the lumbar MAT gradient. DESIGN PARTICIPANTS INTERVENTION: In a prospective randomized crossover trial, 10 healthy men participated in 2 consecutive campaigns of 21days head-down-tilt-bed-rest (HDTBR). They received either whey protein and potassium bicarbonate-supplemented or control diet separated by a 4-month washout period. MAIN OUTCOME MEASURES: Ten serial MRI measures of lumbar vertebral fat fraction (VFF) were performed at baseline, 10days and 20days of HDTBR and 3 and 28days after HDTBR of each bed rest campaign. RESULTS: The mean L5-L1 VFF difference of 4.2 ± 1.2 percentage point higher at L5 (p = 0.008) constituted a caudal to cephalad lumbar MAT gradient. High protein diet did not alter the lumbar VFF differences during both HDTBR campaigns (all time points p > 0.05). Similarly, 2 campaigns of 21days of HDTBR did not change the lumbar VFF differences (all time points p > 0.05). CONCLUSIONS: This pilot study established that the lumbar vertebral MAT gradient was not altered by a high protein nor by 2 × 21days bed rest interventions. These findings demonstrated that this lack of mechanical stimulus was not an important modulator of the lumbar MAT gradient. The highly preserved MAT gradient needs to be measured in more situations of health and disease and may potentially serve to detect pathological situations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".