Alanine Aminotransferase as an Independent Predictor of Incident Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
We read with interest the recent article by Chang et al. (1) reporting that higher serum alanine aminotransferase (ALT) concentrations, within the reference interval, independently predicted the incidence of nonalcoholic fatty liver disease (NAFLD) during a mean follow-up of 2.5 years in a large cohort of apparently healthy Korean men. Several prospective studies have previously shown that increased ALT concentrations, even within the reference interval, also predict the future development of type 2 diabetes (2) and cardiovascular events (3) independently of other known risk factors. In all of these studies, however, increased ALT concentrations have been used as a surrogate marker of NAFLD. Indeed, increased liver enzymes are usually thought to be a consequence (and not a cause) of liver injury in NAFLD and can possibly be used as predictors of NAFLD progression (2). We think the association of the 2 conditions, increased ALT concentrations and incident NAFLD, does not necessarily prove causation. The greater incidence of NAFLD—as diagnosed by ultrasound—among those with slightly increased ALT concentrations at baseline is most likely attributable to an underlying common mechanism, i.e., more severe insulin resistance in those with higher than in those with lower ALT concentrations. That the association between increasing serum ALT concentrations and incident NAFLD remained statistically significant even after adjustment for the homeostasis model assessment (HOMA)-estimated insulin resistance may be due simply to the fact that the HOMA score is not a good proxy measure of insulin resistance. Thus, we wonder how different the results would have been if the euglycemic clamp technique or methods that are more accurate had been used to measure insulin resistance.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".