EXPLORING THE ROLE OF METABOLIC DYSFUNCTION IN ALCOHOL-ASSOCIATED HEPATITIS: A GLOBAL STUDY
Bibliographic record
Abstract
Severe alcohol-associated hepatitis (AH) carries high mortality. Although the role of cardiometabolic risk factors (CMRF)—including obesity, type 2 diabetes mellitus (T2DM), hypertension (HTN), and dyslipidemia (DLP)—has been characterized in steatotic liver disease, their role in the severity of AH remains unclear. To evaluate the impact of CMRF on mortality and infection risk in AH. Multinational prospective cohort study (2015–2024) including hospitalized patients with severe AH across 24 centers in 14 countries (Global AlcHep Network). Diagnosis of AH was done using NIAAA criteria. Analyses included competing-risk models, with liver transplantation as a competing risk. Models were adjusted by age, sex, ethnicity, history of cirrhosis, CMRF, corticosteroids use, MELD, and ACLF grade. 935 participants were included. Median BMI was 24.2kg/m2, prevalence of T2DM was 21%, HTN 17%, DLP 7%. In adjusted competing-risk models, age (sHR 1.02, 95%CI: 1.01-1.04; p<0.001), MELD (sHR 1.04, 95%CI: 1.01–1.06; p<0.001), infections (sHR 1.76, 95%CI: 1.28–2.41; p<0.001), and ACLF grade 2 (sHR 1.67, 95%CI: 1.05–2.69; p<0.032) and 3 (sHR 3.06, 95%CI: 1.88–4.99; p<0.001) were associated with higher risk of mortality, while obesity (sHR 0.67, 95%CI: 0.48–0.93; p=0.016) and corticosteroids use (sHR 0.67, 95%CI: 0.49–0.92; p=0.014) were associated with lower mortality. T2DM, HTN and DLP weren’t associated with higher mortality. Metabolic dysfunction was not associated with increased mortality in AH. Although obesity may be a protective factor, these findings could be explained by a better nutritional status than the lean population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".