Associations of epicardial fat with coronary calcification, insulin resistance, inflammation, and fibroblast growth factor-23 in stage 3-5 chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Epicardial fat, quantified in a single multi-slice computed tomography (MSCT) slice, is a reliable estimate of total epicardial fat volume (EFV). We sought to determine risk factors for EFV detected in a single-slice MSCT measurement (ssEFV) in pre-dialysis chronic kidney disease (CKD) patients. Our primary objective was to determine the association between ssEFV and coronary artery calcification (CAC). METHODS: 94 pre-dialysis stage 3-5 CKD patients underwent MSCT to measure ssEFV and CAC. ssEFV was quantified at the level of the left main coronary artery. Measures of inflammation, traditional and kidney-related cardiovascular disease risk factors were collected. RESULTS: Mean age: 63.7 ± 14 years, 56% male, 39% had diabetes, and mean eGFR: 25.1 ± 11.9 mL/min/1.73 m2. Mean ssEFV was 5.03 ± 2.4 cm3. By univariate analysis, body mass index (BMI) (r = 0.53; P = <0.0001), abdominal obesity (r = 0.51; P < 0.0001), high density lipoprotein (HDL) cholesterol (r = - 0.39; P = <0.0001), insulin resistance (log homeostasis model assessment of insulin resistance (log HOMA-IR)) (r = 0.38, P = 0.001), log interleukin-6 (IL-6) (r = 0.34; P = 0.001), and log urinary albumin to creatinine ratio (UACR) (r = 0.30, P = 0.004) demonstrated the strongest associations with ssEFV. Log coronary artery calcification (log CAC score) (r = 0.28, P = 0.006), and log fibroblast growth factor-23 (log FGF-23) (r = 0.23, P = 0.03) were also correlated with ssEFV. By linear regression, log CAC score (beta =0.40; 95% confidence interval (CI), 0.01-0.80; P = 0.045), increasing levels of IL-6 (beta = 0.99; 95% CI, 0.38 - 1.61; P = 0.002), abdominal obesity (beta = 1.86; 95% CI, 0.94 - 2.8; P < 0.0001), lower HDL cholesterol (beta = -2.30; 95% CI, - 3.68 to -0.83; P = 0.002) and albuminuria (log UACR, beta = 0.81; 95% CI, 0.2 to 1.4; P = 0.01) were risk factors for increased ssEFV. CONCLUSIONS: In stage 3-5 CKD, coronary calcification and IL-6 and were predictors of ssEFV. Further studies are needed to clarify the mechanism by which epicardial fat may contribute to the pathogenesis of coronary disease, particularly in the CKD population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".