Biomarkers in Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a chronic liver condition characterized by insulin resistance, type 2 diabetes and fat accumulation in the liver that may cause hepatic inflammation and progressive scarring leading to nonalcoholic steatohepatitis (NASH) and irreversible liver damage (cirrhosis). As a result, there has been increased recognition of the need to assess and closely monitor individuals for risk factors of components of NAFLD and NASH, as well as the severity of these conditions using biomarkers. AIM: To review the biomarkers used to diagnose and define the severity of NAFLD and NASH. METHODS: A comprehensive PubMed and Google Scholar literature search was performed using the terms "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", as well as the name of each biomarker known to be used. Articles indexed between 2004 and 2014 were used. Each author read the publications separately and the results were discussed. RESULTS: Biomarkers offer a potential prognostic or diagnostic indicator for disease manifestation, progression or both. Serum biomarkers, including total cholesterol, triglycerides, insulin resistance and C-peptide, have been used for many years. Emerging biomarkers, such as apolipoprotein A1, apolipoprotein B, leptin, adiponectin, free fatty acids, ghrelin and tumour necrosis factor-alpha, have been proposed as tools that could provide valuable complementary information to that obtained from traditional biomarkers. Moreover, markers of cell death and mitochondrial dysfunction (cytokeratins) represent powerful predictors of risk. For biomarkers to be clinically useful in accurately diagnosing and treating disorders, age-specific reference intervals that account for differences in sex and ethnic origin are a necessity. CONCLUSIONS: The present review attempts to provide a comprehensive analysis of the emerging risk biomarkers of NAFLD and NASH, and to use the clinical significance and analytical considerations of each biomarker pointing out sentinel features of disease progression.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".