Changes in fat and skeletal muscle with exercise training in obese adolescents: Comparison of whole‐body MRI and dual energy X‐ray absorptiometry
Bibliographic record
Abstract
OBJECTIVE: We examined skeletal muscle (SM) and fat distribution using whole-body MRI in response to aerobic (AE) versus resistance exercise (RE) training in obese adolescents and whether DXA provides similar estimates of fat and SM change as MRI. DESIGN AND METHODS: Thirty-nine obese boys (12-18 years) were randomly assigned to one of three 3-month interventions: AE (n = 14), RE (n = 14), or a control (n = 11). RESULTS: At baseline, MRI-measured total fat was significantly greater than DXA-measured total fat [△ = 3.1 kg (95% CI: -0.4 to 7.4 kg, P < 0.05)], wherein underestimation by dual energy X-ray absorptiometry (DXA) was greatest in those with the highest total fat. Overall, the changes in total fat were not significantly different between MRI and DXA [△ = -0.4 kg (95% CI: -3.5 to 2.6 kg, P > 0.05)], but DXA tended to overestimate MRI fat losses in those with larger fat losses. MRI-measured SM and DXA-measured lean body mass were significantly correlated, but as expected the absolute values were different at baseline [△ = -28.4 kg (95% CI: -35.4 to -21.3 kg, P < 0.05)]. Further, DXA overestimated MRI gains in SM in those with larger SM gains. CONCLUSIONS: Although DXA and MRI-measured total and regional measures tended to be correlated at baseline and changes with exercise, there were substantial differences in the absolute values derived using DXA versus MRI. Further, there were systemic biases in the estimation between the methods wherein DXA tended to overestimate fat losses and SM gains compared to MRI. Thus, the changes in body composition observed are influenced by the method employed.
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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 | 0.001 | 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 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".