Plasma Gamma-Glutamyltransferase Is Strongly Determined by Acylation Stimulating Protein Levels Independent of Insulin Resistance in Patients with Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND: Steatosis is a manifestation of the metabolic syndrome often associated with release of liver enzymes and inflammatory adipocytokines linked to cardiovascular risk. Gamma-glutamyltransferase (GGT) is one sensitive liver marker recently identified as an independent cardiovascular risk factor. Mechanisms involved in enhanced hepatic lipogenesis causing steatosis are not yet identified and are usually linked to insulin resistance (IR). Acylation stimulating protein (ASP), a potent lipogenic factor, was recently shown to increase in patients with steatosis and was implicated in its pathogenesis. AIM: To investigate the association of plasma ASP levels with liver and metabolic risk markers in acute coronary syndrome (ACS) patients. METHODS: 28 patients and 30 healthy controls were recruited. Their anthropometrics, lipid profile, liver markers, insulin, and ASP levels were measured. RESULTS: In the patients, ASP, liver, and metabolic risk markers were markedly higher than in the controls. ASP strongly predicted GGT levels (B = 0.75, P < 0.0001), followed by triglycerides (B = 0.403, P = 0.017), together determining 57.6% variation in GGT levels. Insulin and IR correlated with metabolic risk components but not with liver enzymes. CONCLUSION: The strong association of ASP with GGT in ACS patients suggests that ASP, independent of IR, may contribute to a vicious cycle of hepatic lipogenic stimulation and GGT release promoting atherogenesis.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".