The association of serum phospholipids levels with chronic liver diseases: A systematic review of observational studies
Bibliographic record
Abstract
Background: Chronic liver disease (CLD) influences the levels of diverse metabolites that may be related to its pathogenesis. The study aimed to indicate the relation between CLD and the levels of phospholipids. Methods: In this systematic review, PRISMA guidelines were considered for reporting the results. Up to November 2024, the databases of MEDLINE (through PubMed), Scopus, Web of Science, and Google Scholar were searched. Case-control (CC) and cross-sectional (CS) studies explored the link between CLD and serum phospholipids. The Newcastle-Ottawa scale (NOS) for CC studies and the modified NOS scale for CS studies were applied to evaluate the quality of the included articles. Results: A total of 11304 articles were included. Eleven thousand duplicates were removed, 9304 studies were excluded, and 343 full-text articles were reviewed. Fifteen CC studies and four CS studies were included in this study. Quality assessment using NOS revealed most studies had low to moderate risk of bias, with scores ranging from 4 to 8 out of 9.The included studies verified a significant association between the levels of total PL (TPL), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA), lysophosphatidylcholine (LPC), lysophosphatidylinositol (LPI) and lysophosphatidic acid (LPA) and liver diseases., with reported odds ratios ranging from 1.44 to 2.51 and correlation coefficients from -0.58 to 0.62. Conclusion: Phospholipid levels are associated with liver diseases. It is important to identify noninvasive ways to diagnose biological risk factors in patients with liver damage so they can be targeted for early treatment. Most of the included studies revealed significant alteration of phospholipid levels in CLD. Thus, the lipidome can predict liver dysfunction and prevent its attributed complications.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".