Association Between Visceral Fat Accumulation and Exercise Tolerance in Non-Obese Subjects Without Diabetes
Bibliographic record
Abstract
BACKGROUND: We examined the associations between visceral fat accumulation, presence of the components of metabolic syndrome (MetS), and exercise tolerance in non-obese subjects without diabetes. METHODS: Seventy-four non-obese, non-diabetic Japanese men were enrolled. The subjects were divided into the following two groups: non-obese subjects without any MetS risk factors (n = 38, Group A) and non-obese subjects with one or two MetS risk factors (n = 36, Group B). Anthropometric and metabolic parameters were measured. The response of heart rate (HR) and blood pressure (BP), and exercise tolerance were also evaluated with a cardiopulmonary exercise test using a bicycle ergometer. RESULTS: The body mass index, abdominal circumference, visceral fat area, and homeostasis model assessment-insulin resistance, were significantly higher, while levels of anaerobic threshold and maximal oxygen uptake were significantly lower in Group B than in Group A. The levels of resting HR, resting BP, and BP at maximal exercise were significantly higher in Group B than in Group A. There were no significant differences in the HR at maximal exercise as well as the HR and BP after exercise between the two groups. The visceral fat area was significantly and negatively correlated with exercise tolerance. Multivariate linear regression analyses demonstrated that visceral fat area, but not abdominal circumference, was significantly and independently associated with maximal oxygen uptake. CONCLUSIONS: These data suggest that the visceral fat area is a significant determinant for exercise tolerance even in non-obese subjects without diabetes.
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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 | 0.001 |
| 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) | 0.002 | 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".