Cardiometabolic risk factors in type 2 diabetes with high fat and low muscle mass: At baseline and in response to exercise
Bibliographic record
Abstract
Objective To examine the interplay between high fat and low muscle mass on cardiometabolic risk factors at baseline and in response to exercise in type 2 diabetes. Methods Using baseline percent body fat and skeletal muscle mass index, 248 participants were divided into high fat versus low fat and low muscle versus high muscle. Linear mixed models were used to examine the main effects of fat mass and muscle mass and their interaction on baseline cardiometabolic risk factors and changes in risk factors in response to 6‐month exercise training. Results At baseline, there was a fat mass by muscle mass interaction effect on hemoglobin A1c (HbA1c; P = 0.009), suggesting that low muscle was associated with elevated HbA1c in those with high fat but not low fat. Significant interactions between baseline fat mass and muscle mass on the exercise‐induced changes in visceral adipose tissue, insulin resistance, and triglyceride concentration indicated that low muscle attenuated the exercise responses in participants with high fat at baseline (all P < 0.05). Conclusions High fat and low muscle may be synergistically associated with higher HbA1c in type 2 diabetes. In participants with high fat mass, low muscle mass may attenuate exercise‐induced improvements in some cardiometabolic risk factors.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".