Improvement of non-invasive tests of liver steatosis and fibrosis as indicators for non-alcoholic fatty liver disease in type 2 diabetes mellitus patients with elevated cardiovascular risk profile using the PPAR-α/γ agonist aleglitazar
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Peroxisome proliferator-activated receptor (PPAR) agonists may have favorable outcomes on non-alcoholic fatty liver disease. This study serves as proof of concept to evaluate whether dual PPAR-α/γ agonists improve non-invasive tests of liver steatosis and fibrosis. METHODS: This is a post-hoc analysis of a randomized, double-blind, placebo-controlled, multi-center trial comprising 7226 patients with type 2 diabetes mellitus and recent coronary artery disease randomized to receive aleglitazar, a PPAR-α/γ agonists, or placebo for two years. Main outcomes were change in non-invasive tests for liver steatosis and fibrosis: Liver Fat Score (LFS), Liver Accumulation Product (LAP), Fibrosis-4 (FIB-4), and NAFLD Fibrosis Score (NFS). RESULTS: LFS, LAP and FIB-4 decreased upon treatment, whereas scores in the placebo group remained the same or increased (P<0.001). NFS responded differently but remained consistently lower than placebo. In the treatment group more participants shifted to a lower FIB-4 and NFS category, or improved in respect to the LAP cut-off values compared to the placebo group (P<0.001 for FIB-4 and LAP, P<0.004 for NFS). LFS had a low discriminative power in this study. CONCLUSION: This post-hoc analysis showed improvement of non-invasive tests of liver steatosis and fibrosis after starting dual PPAR-α/γ agonist treatment, adding to the evidence that this pathway has potential in non-alcoholic fatty liver disease treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".