# Schematic Assessment of Metabolic Signatures of Non-alcoholic Fatty Liver Disease by Bridging Endocrinology and Internal Medicine: A Precision Therapy-Based Meta-Analysis

## TL;DR

This study reviews how metabolic signatures can help understand and treat non-alcoholic fatty liver disease, but finds inconsistencies that limit their current use.

## Contribution

The paper introduces a meta-analysis linking endocrinology and internal medicine to assess metabolic signatures for NAFLD precision therapy.

## Key findings

- Metabolomic profiling identified amino acids, lipids, and gut metabolites linked to NAFLD severity.
- Meta-analysis found no significant association between metabolic signatures and disease progression (HR 0.98).
- High heterogeneity (I² = 82%) and low evidence certainty limit clinical applicability of findings.

## Abstract

Non-alcoholic fatty liver disease (NAFLD) is seen as a health concern globally and is identified via complex interactions of metabolic dysfunctions. Metabolomic and lipidomic profiling has been emerged as a promising tool for non-invasive diagnosis and precision therapy. This systematic review and meta-analysis aimed to assess the affect of metabolic signatures associated with NAFLD progression and their utility in paving path for precision medicine. A comprehensive literature search was conducted in adherence to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Appropriate data studies were pooled to check the disease progression using a random effects model. Risk of bias and certainty of evidence were assessed using the Cochrane risk of bias tool, ROBINS-I (“Risk Of Bias In Non-randomized Studies - of Interventions”), and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework respectively. Studies found distinct metabolite patterns especially in amino acids, lipids, and gut-derived metabolites that correlated with the severity of NAFLD. The meta-analysis findings revealed a pooled hazard ratio of 0.98 (95% CI: 0.83-1.15) that indicated that no significant association was found between studies for assessment of metabolic signatures and their link to disease progression. High heterogeneity was observed (I² = 82%). Risk of bias was generally low to moderate, but overall certainty of evidence was rated low to moderate due to inconsistency and imprecision. Metabolic profiling offered valuable insights and discoveries into pathophysiology of NAFLD and stratification. However, high heterogeneity found across studies limited current clinical applicability. Standardized methodologies and longitudinal validation were needed to combine metabolic signatures into precision NAFLD care.