Metabolic Dysfunction‐Associated Steatotic Liver Disease ( <scp>MASLD</scp> ): Mechanisms, Clinical Implications and Therapeutic Advances
Bibliographic record
Abstract
INTRODUCTION: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has emerged as the most prevalent chronic liver disease worldwide, affecting ~25%-30% of the adult population, with higher prevalence observed in individuals with obesity and type 2 diabetes. Among reported MASLD cases, prevalence is consistently higher in men than in women, and global incidence has risen by ~50% over the past two decades, mirroring the global rise in obesity and metabolic syndrome. MASLD encompasses a spectrum of hepatic pathologies ranging from simple steatosis to steatohepatitis, fibrosis and cirrhosis. Despite its high prevalence, the heterogeneity in disease progression and relative absence of approved pharmacological therapies pose challenges for effective clinical management. METHODS AND RESULTS: This review synthesises current literature on MASLD across epidemiology, pathophysiology, clinical presentation and treatment. Key molecular mechanisms, including lipid metabolism dysregulation, insulin resistance and mitochondrial dysfunction, are examined with a focus on understanding the basis for progression to metabolic dysfunction-associated steatohepatitis (MASH). Clinical manifestations, diagnostic tools and risk stratification systems for MASLD are summarised. Current and emerging therapies such as lifestyle interventions, pharmacological agents and microbiome-targeted strategies are reviewed. The review also highlights ongoing challenges, including diagnostic limitations, disease heterogeneity and disparities in care. CONCLUSION: MASLD is a complex, multifactorial liver disease with a growing public health impact, driven by the rising prevalence of metabolic syndrome. Mitochondrial dysfunction is a critical nexus linking genetic susceptibility to metabolic stress and inflammatory responses. Preclinical models that capture these mitochondrial contributions are vital for therapeutic discovery and for advancing personalised medicine approaches in MASLD care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".