Effects of empagliflozin on markers of liver steatosis and fibrosis and their relationship to cardiorenal outcomes
Bibliographic record
Abstract
AIMS: Empagliflozin treatment reduced liver fat in small type 2 diabetes cohorts. This post-hoc study evaluated effects of empagliflozin on risk for non-alcoholic fatty liver disease-related steatosis and fibrosis, as well as the relationship between risk categories and cardiorenal outcomes in the randomized, placebo-controlled EMPA-REG OUTCOME trial. MATERIALS AND METHODS: EMPA-REG OUTCOME treated 7020 people with type 2 diabetes and cardiovascular disease with 10/25 mg/day empagliflozin or placebo. For this analysis, the Dallas steatosis index, hepatic steatosis index, non-alcoholic fatty liver disease fibrosis score and Fibrosis-4 score were calculated to assess steatosis and fibrosis risk. Changes from baseline in scores were examined by mixed model repeated measures and their associations with cardiorenal outcomes and mortality by Cox regression. RESULTS: At baseline, 73% and 84% of participants had high steatosis risk by Dallas steatosis index and hepatic steatosis index, whereas 23% and 4% had a high risk of advanced fibrosis by non-alcoholic fatty liver disease fibrosis score and Fibrosis-4 score. Percentages of people at high steatosis risk slightly decreased with empagliflozin only, whereas empagliflozin did not improve percentages of individuals at high fibrosis risk over time compared with placebo. The high risk of advanced fibrosis at baseline related to higher risk for cardiovascular events. Effects of empagliflozin on cardiorenal and all-cause mortality outcomes were consistent across all risk groups. CONCLUSIONS: Empagliflozin may reduce steatosis but not fibrosis risk in individuals with type 2 diabetes and cardiovascular disease. The improvements in cardiorenal outcomes and mortality associated with empagliflozin therapy appear to be independent of steatosis and fibrosis risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".