#6732 EPICARDIAL ADIPOSE TISSUE AND REVERSE EPIDEMIOLOGY IN DIALYSIS PATIENTS
Bibliographic record
Abstract
Abstract Background and Aims Conventional risk factors of cardiovascular disease and mortality in the general population such as body mass is relate to adverse outcome in dialysis patients, but often in an opposite direction (reverse epidemiology). On the contrary, epicardial adipose tissue (EAT) has reported to have a local inflammatory and proatherogenic effect. Thus, the association of EAT as direct measures of region specific adipose tissue in chronic kidney disease (CKD) has been studied. Method The study evaluated 37 CKD-5 patients in hemodialysis (mean dialysis duration 26±30 months, 70% males, 57% African-American, 48% diabetic) that underwent MSCT to measure EAT and coronary artery calcium (CAC) score. CAC distribution in quartiles of EAT was assessed. Finally, we measured patients’ body mass index (BMI) and categorized them as normal (BMI 18.5-25 kg/m2) and abnormal BMI (BMI >25 kg/m2). Results The mean BMI was 28.6±5.7 kg/m2, and the mean EAT volume was 66±29 ml and 102±69 ml for normal and abnormal BMI groups. There was a direct correlation between dialysis vintage and EAT while BMI showed an inverse relationship with dialysis vintage (p = 0.05). In this small sample size, the association of CAC and EAT was only marginally significant (p = 0.7). EAT was significantly correlated with age (p = 0.02), history of cardiovascular disease (p = 0.03) and diabetes (p = 0.001). Conclusion The direct association of EAT with dialysis vintage in the face of an inverse association of BMI with dialysis duration appear to support the notion that chronic hemodialysis is a state of chronic inflammation and malnutrition.
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 | 0.000 | 0.001 |
| 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) | 0.004 | 0.001 |
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".