Substantial Changes in Epicardial Fat Thickness After Weight Loss in Severely Obese Subjects
Bibliographic record
Abstract
We sought to evaluate the effect of weight loss on echocardiographic epicardial fat thickness, as index of visceral adiposity, and whether epicardial fat change after the weight loss can be proportionally different from overall body weight changes and related to cardiac parameters changes in severely obese subjects. This was an interventional study in 20 severely obese subjects (12 women, 8 men, BMI 45+/-5 kg/m(2), 35+/-10 years) who underwent 6-month very low calorie diet weight loss program. Baseline and after 6-month weight loss anthropometrics, echocardiographic epicardial fat thickness, left ventricular mass (LVM), and diastolic function parameters were assessed. Subjects lost 20% of original body weight, BMI reduced by 19% of original BMI, waist circumference decreased by 23% of initial waist circumference. Epicardial fat thickness decreased from 12.3+/-1.8 to 8.3+/-1 mm P<0.001 after the 6-month very low calorie diet, as -32% of baseline epicardial fat thickness. LVM and diastolic function changes were better correlated with epicardial fat changes. We showed that significant weight loss can be associated with significant reduction in the epicardial fat thickness, marker of visceral adiposity in severely obese subjects. Epicardial fat decrease, therefore visceral fat decrease, can be proportionally higher than overall adiposity decrease. Epicardial fat changes are significantly associated with obesity-related cardiac morphological and functional changes during weight loss. Measurement of echocardiographic epicardial fat thickness may provide an additional tool in understanding the metabolic risk associated with variation in fat distribution.
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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) | 0.001 | 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".