Relation of Epicardial Fat and Alanine Aminotransferase in Subjects With Increased Visceral Fat
Bibliographic record
Abstract
BACKGROUND: Increased visceral adipose tissue (VAT) is a risk factor for an unfavorable cardio-metabolic profile and fatty liver. Individuals with human immunodeficiency virus (HIV) on highly active antiretroviral therapy (HAART) can be associated with metabolic syndrome (MS) and higher visceral fat. However, the potential link between cardiac adiposity, emerging index of visceral adiposity, and fatty liver is still unexplored. OBJECTIVE: To evaluate whether echocardiographic epicardial adipose tissue, index of cardiac adiposity, could be related to serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity, surrogate markers of fatty liver, in HIV-infected patients with (HIV+MS+) and without HAART-associated MS (HIV+MS-). METHODS AND PROCEDURES: This was a cross-sectional observational study on 57 HIV+MS+ patients, 52 HIV+MS- and 57 HIV-negative subjects with MS (HIV-MS+), as control group. Epicardial fat thickness and intra-abdominal VAT were obtained by echocardiography and magnetic resonance imaging (MRI), respectively. Serum ALT and AST activity, plasma adiponectin levels, and MS biochemical parameters were measured. RESULTS: Echocardiographic epicardial fat thickness was correlated with MRI-VAT (r = 0.83, P < 0.01), AST/ALT ratio (r = 0.77, P < 0.01), ALT (r = 0.58, P < 0.01), AST (r = 0.56, P < 0.01), and adiponectin (r = -0.45, P < 0.01) in HIV+MS+. MRI-VAT and AST/ALT ratio were the best correlates of epicardial fat thickness (r (2) = 0.45, P < 0.01). DISCUSSION: This study shows for the first time a clear relationship of epicardial fat, index of cardiac and visceral adiposity, and serum ALT and AST activity, markers of fatty liver, in subjects with increased visceral adiposity and cardio-metabolic risk. This correlation seems to be independent of overall adiposity and rather function of excess visceral adiposity.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".