Efficacy of Non-invasive Biomarkers in Diagnosing Non-alcoholic Fatty Liver Disease (NAFLD) and Predicting Disease Progression: A Systematic Review
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease, with significant global prevalence and a strong association with metabolic syndrome, obesity, and diabetes. Early diagnosis and prediction of disease progression are critical for effective management. Non-invasive biomarkers have emerged as promising alternatives to liver biopsy, offering safer and more accessible diagnostic and prognostic options. This systematic review evaluates the efficacy of non-invasive biomarkers in diagnosing NAFLD and predicting disease progression, focusing on diagnostic accuracy, clinical utility, and limitations. A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, including studies published between 2010 and 2024. Databases such as PubMed, Embase, and Scopus were searched using relevant keywords and Boolean operators. Inclusion criteria comprised studies evaluating adults (18+) with NAFLD using non-invasive biomarkers, compared to liver biopsy or other standards, and reporting diagnostic metrics such as sensitivity, specificity, and area under the curve (AUC). Data were extracted systematically, and study quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) and the Newcastle-Ottawa Scale. The nine studies include a range of biomarkers such as serum markers (Pro-C3, NIS4), imaging techniques (MRI-PDFF, cT1), and composite scores (cTAG, NFS). Diagnostic accuracy was high, with area under the curve (AUC) values ranging from 0.81 to 0.90 for detecting significant fibrosis and at-risk non-alcoholic steatohepatitis (NASH). Imaging tools such as MRI-PDFF offered superior reproducibility and whole-liver assessments, while composite biomarkers such as NIS4 demonstrated robust sensitivity but moderate specificity. Notable heterogeneity in populations and methodologies was observed. Non-invasive biomarkers show comparable diagnostic performance to liver biopsy while offering significant advantages in safety, scalability, and patient adherence. However, gaps remain, including the need for validation in diverse populations and improved specificity for advanced fibrosis. Integrating non-invasive biomarkers into clinical practice could revolutionize NAFLD management by enabling early diagnosis, guiding treatment, and reducing reliance on invasive methods. Future research should focus on validating these tools across diverse cohorts and developing novel biomarkers to address existing limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".