The Relationship Between Epicardial Fat and Indices of Obesity and the Metabolic Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Epicardial fat (epicardial adipose tissue, EAT) has been implicated in the pathogenesis of coronary artery disease (CAD). The objective of this study was to examine the relationship between EAT and generalized obesity, central or visceral adipose tissue (VAT), and the components of the metabolic syndrome--systolic blood pressure (SBP), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FBG)--that are linked to CAD. A systematic review of the literature, following meta-analysis guidelines, was conducted until May, 2013, using the search strategy "Obesity" OR "abdominal obesity" OR "metabolic syndrome" OR "metabolic syndrome X" AND "epicardial fat". Thirty-eight studies fulfilled the criteria. There was a highly significant (P<0.00001) correlation between EAT and body mass index (BMI), waist circumference (WC), or VAT. The correlation between EAT and VAT was significantly (P<0.0001) greater than the correlation between EAT and WC, which in turn was significantly greater than the correlation between EAT and BMI. Overall, EAT was 7.5 ± 0.1 mm in thickness in the metabolic syndrome (n=427) compared to 4.0 ± 0.1 mm in controls (n=301). EAT correlated significantly (P<0.0001) with SBP, TGs, HDL, and FBG, but the strength of the association was less than one-half of the relationship of EAT to indices of obesity. The results of multivariate analysis were less consistent but show a relationship between EAT and metabolic syndrome independent of BMI. In summary, the very strong correlation between EAT and VAT suggests a relationship between these two adipose tissue depots. Measurement of EAT can be useful to indicate VAT. Whereas EAT correlates significantly with each of the components of the metabolic syndrome- SBP, TGs, HDL, or FBG-the magnitude of the relationship is considerably and significantly less than the relationship of EAT to BMI. These data show the strong relationship between EAT and BMI but especially with WC and VAT. They also demonstrate the smaller magnitude of the association of EAT with standard coronary risk factors, related to the metabolic syndrome, and suggest that the unique features of this adipose tissue warrant detailed investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".