A new perspective on non-invasive diagnosis of non- alcoholic fatty liver disease: evidence integration of inflammatory and metabolic biomarkers based on a scoping review
Bibliographic record
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver condition globally, spanning a spectrum from simple steatosis to non-alcoholic steatohepatitis (NASH) and progressive fibrosis. Inflammation and metabolic dysregulation play key roles in its pathogenesis. Accordingly, inflammatory and metabolic biomarkers have gained increasing attention as potential tools for non-invasive diagnosis and disease staging. Objective: This scoping review aimed to synthesize current evidence on the diagnostic performance of inflammatory and metabolic biomarkers for NAFLD, with a focus on their potential application in early screening and disease stratification. Methods: We systematically searched PubMed and CNKI databases for relevant peer-reviewed literature published up to August 2024. The search strategy combined MeSH terms and free-text keywords, and study selection was guided by the Population-Concept-Context (PCC) framework. Methodological quality was assessed using the Newcastle-Ottawa Scale and AHRQ criteria. Results: Fifteen eligible studies (11 case-control, 2 cohort, 1 cross-sectional, and 1 retrospective study) were included, yielding 18 candidate biomarkers. The triglyceride-glucose (TyG) index was commonly associated with early-stage NAFLD screening; cytokeratin-18 (CK18) was linked to NASH detection, while adiponectin and osteopontin (OPN) were related to liver fibrosis. Additionally, inflammatory indices such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII) showed clinical promise due to their accessibility and low cost. Conclusion: Inflammatory and metabolic biomarkers provide valuable non-invasive insights into the diagnosis and staging of NAFLD. The integration of multiple biomarkers may enhance diagnostic accuracy and support stratified management strategies. However, further validation is needed to establish standardized thresholds and confirm clinical utility across diverse populations.
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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 | 0.086 | 0.185 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.038 | 0.031 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".