Comparative analysis of serum amino acid levels and their potential as biomarkers in liver cirrhosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Early detection and precise prevention are crucial for reducing complications and mortality in liver cirrhosis (LC). The predictive value of amino acids profiles in monitoring the development of LC lacks consistent and comprehensive conclusions. This study aimed to review existing studies utilizing metabolomics to detect serum amino acid levels in LC patients. Meta-analysis was performed to identify potential biomarkers predictive of LC. Literature retrieved from eight databases between January 1, 2000 and August 1, 2025 was screened according to the inclusion criteria. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the risk of bias in the included studies. A random-effects model for amino acid concentrations in the meta-analysis was used to calculate mean differences (MD) and 95% confidence intervals (95% CI). The I2 statistic was used to measure study heterogeneity. This study has been registered with the PROSPERO. A total of 2125 records were retrieved, and 21 studies with 2254 individuals were included after screening, all of which were of high quality. Qualitative analysis of 21 studies including 23 amino acids. The meta-analysis of five studies showed that six amino acids (tyrosine, methionine, ornithine, threonine, citrulline and tryptophan) were significantly increased in the LC group, whereas two amino acids (arginine and valine) were significantly decreased. Subgroup analysis suggested that etiology and detection methods may be sources of heterogeneity in the results. This manuscript will provide a comprehensive of differential expression of amino acid profiles and provide potential theoretical evidence to subsequently guide the assessment and treatment of LC.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".