Differences in Gene Expression and Gene Associations in Epicardial Fat Compared to Subcutaneous Fat
Bibliographic record
Abstract
Epicardial adipose tissue (EAT) plays a role in cardiac physiology and may contribute to the development of coronary artery disease. Our objective was to determine whether there was a significant difference in gene expression in EAT compared to subcutaneous adipose tissue (SAT) and to identify potential relationships. MEDLINE and EMBASE were searched using the key terms "Epicardial Adipose Tissue" or "Epicardial Fat" in combination with "RNA", "mRNA", or "gene". The entry criteria were studies that presented primary data including expression levels of mRNA in human EAT compared with SAT and an expression of variance (SD). Genes identified by 2 or more studies were evaluated. Genes that showed significant change in expression between EAT and SAT were examined using the Gene Functional Classification analytical tool in Database for Annotation, Visualization and Integrated Discovery and cross-validated by ToppGene. Seventeen genes were identified from 25 studies. Meta-analysis showed that 10 genes (ADORA1, adiponectin, AGT, ADM, CATA, IL-1β, MCP-1, RBP-4, TNF-α, UCP-1) were significantly different in EAT. Gene Functional Classification analysis yielded 23 clusters with significant relationships. The top clusters were focused on responses to glucocorticoid stimulus, regulation of apoptosis, cellular ion homeostasis, and responses to hormone stimulus. Genetic analysis shows that EAT is discretely different from SAT. ADORA1, adiponectin, AGT, ADM, CATA, IL-1β, MCP-1, RBP-4, TNF-α, and UCP-1 may play significant roles in the unique physiology of EAT and/or its role in pathophysiology, through mechanisms as diverse as steroid hormone responses and regulation of apoptosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".