Epicardial Adipose Tissue Thickness as a Reliable Marker of Increased Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Epicardial adipose tissue (EAT) is a distinct visceral adipose tissue that is present in between the visceral pericardium and myocardium, in which the coronary arteries are lodged. This tissue releases several inflammatory and atherogenic mediators which lead to the initiation and/or progression of coronary atherosclerosis and its thickness is related to the presence and severity of atherosclerotic coronary artery disease (CAD). In this study, we aimed to evaluate the EAT thickness in type 2 diabetes mellitus (T2DM) patients by non-contrast computed tomography (NCCT) of the chest and to correlate its value with HbA1c levels, duration of T2DM and carotid intima-media thickness (CIMT). Methods: This was a cross-sectional study comprising of 40 patients with T2DM who underwent NCCT of chest and ultrasonographic estimation of CIMT. Ten individuals with equivalent age and sex were included as controls, in whom NCCT of chest was done for other purposes. All the individuals underwent thorough history, clinical examination and certain investigations based on a predesigned proforma. Results: The EAT thickness was significantly increased in patients with T2DM as compared to controls (8.7 ± 2.94 mm vs. 3.48 ± 0.99 mm, P < 0.001) and also strongly correlated with duration of diabetes (P = 0.02), HbA1c (P is less than or equal to 0.001), total cholesterol (P is less than or equal 0.001), serum triglyceride levels (P is less than or equal 0.001) and body mass index (BMI) (P is less than or equal 0.001). Conclusion: EAT thickness can be regarded as a sensitive and non-invasive marker for risk stratification of CAD. J Endocrinol Metab. 2020;10(6):173-181 doi: https://doi.org/10.14740/jem710
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".