The association of serum phospholipids levels with chronic kidney disease: A systematic review of observational studies
Bibliographic record
Abstract
Introduction: Chronic kidney disease (CKD) affects the levels of various metabolites, which may be associated with pathogenesis of chronic diseases. This study aimed to indicate the association between CKD and changes in the levels of phospholipids. Methods: This systematic review considered the PRISMA guidelines for reporting the results. We searched the databases MEDLINE (via PubMed), Scopus, Web of Sciences and Google Scholar until June 2023. Case-control and cross-sectional studies investigated the relationship between CKD and alterations of serum levels of phospholipids. We determined the quality of the articles using the modified Newcastle-Ottawa scale (NOS) for cross-sectional studies and the NOS scale for case-control studies. Results: A total of 28977 articles were included. One hundred and fifty duplicated articles were removed, 28827 studies were excluded, 343 full-text articles were reviewed and sixteen studies were included at the end. The studies demonstrated a significant association between serum levels of total phospholipids (TPLs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidic acid (PA) and plasmalogen with renal diseases. Conclusion: Phospholipids levels are associated with the kidney diseases. It is important to identify non-invasive ways to diagnose biological risk factors in patients with renal damages, so they can be targeted for early treatment. The included studies reported significant alteration of phospholipids levels in CKD.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".