Changes in Body Fat Distribution and Fitness Are Associated With Changes in Hemoglobin A1c After 9 Months of Exercise Training
Bibliographic record
Abstract
OBJECTIVE: To investigate the associations between changes in body composition and fitness after exercise training and changes in hemoglobin A1c (HbA1c) in individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS: Participants (n = 201) were randomized to aerobic, resistance, or combined training for 9 months. HbA1c, waist circumference, total and trunk fat mass, appendicular fat mass, lean body mass, isokinetic leg muscle strength, peak O2 uptake, and estimated METs were assessed at baseline and follow-up. Change in HbA1c was evaluated across quartiles of change in body composition and fitness. RESULTS: Change in HbA1c was associated with changes in body weight (r = 0.13, P = 0.052), waist circumference (r = 0.17, P = 0.013), trunk fat mass (r = 0.19, P = 0.005), and estimated METs (r = -0.16, P = 0.023). There was a trend in change in HbA1c across quartiles of waist circumference (P = 0.011), trunk fat mass (P = 0.020), and estimated METs (P = 0.011). Participants with increased estimated METs and reduced trunk fat mass had greater odds of having reduced HbA1c after training (3.48, 1.46-8.31). Finally, participants with increased estimated METs and reduced waist circumference were 2.81 (1.13-6.98) times more likely to have reduced HbA1c and type 2 diabetes medication use than those without improved fitness and central adiposity. CONCLUSIONS: In patients with type 2 diabetes, a reduction in central adiposity and increase in fitness were the most prominent predictors of the change in HbA1c in response to exercise training.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".