Comparison of reducing epicardial fat by exercise, diet or bariatric surgery weight loss strategies: a systematic review and meta‐analysis
Bibliographic record
Abstract
The objectives were to determine whether epicardial fat (EAT) is subject to modification, and whether various strategies accomplish this end point and the relationship between weight loss and EAT. A systematic review of the literature following meta-analysis guidelines was conducted using the search strategy 'epicardial fat' OR 'epicardial adipose tissue' AND 'diet' OR 'exercise' OR 'bariatric surgery (BS)' OR 'change in body weight' limited to humans. Eleven articles were identified with 12 intervention approaches of which eight studies showed a statistically significant reduction in EAT. A random-effects meta-analysis suggests an overall significant reduction of 1.12 standardized units (95% CI = [-1.71, -0.54], P value < 0.01). While there is a large amount of heterogeneity across study groups, a substantial amount of this variability can be accounted for by considering intervention type and change in body mass index (BMI). These variables were incorporated into a random-effects meta-regression model. Using this analysis, significant EAT reduction occurred with diet and BS but not with exercise. BMI reductions correlated significantly with EAT reductions for diet-based interventions, i.e. for some but not all interventions. In conclusion, EAT, a factor that is significantly associated with coronary artery disease, can be modified. The type of intervention, in addition to the amount of weight loss achieved, is predictive of the amount of EAT reduction.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".