Chemerin levels in chronic kidney disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Chemerin as an inflammatory biomarker has gained attention in its biomarker capability. Several studies measured its levels in chronic kidney disease (CKD), as one of the common non-communicable causes of mortality and morbidity. Hence, this systematic review and meta-analysis aimed to investigate this association. Methods: PubMed, Scopus, Embase, and the Web of Science databases were systematically searched for studies investigating chemerin levels in any CKD stage (including end-stage renal disease patients undergoing hemodialysis (HD)) and comparing it with healthy controls. Random effect meta-analysis was performed to calculate the standardized mean difference (SMD) and 95% confidence interval (CI). Results: A total of eight studies were included, comprised of 875 individuals, with a mean age of 56.92 ± 11.78 years. All studies had high quality based on the New Castle-Ottawa Scale (NOS). Meta-analysis revealed significantly higher levels of chemerin in CKD patients compared to healthy controls (SMD 2.15, 95% CI 0.83-3.48, p-value<0.01). Additionally, HD patients had statistically higher levels of chemerin than controls (SMD 2.10, 95% CI 0.58-3.62, p-value=0.01). In meta-regression, publication year accounted for 23.50% and 24.17% of heterogeneity for these analyses, respectively. Conclusion: Chemerin can be potentially used as a biomarker in CKD patients, which can suggest the inflammatory pathways for the disease. Further research is warranted for the assessment of its clinical applications and enlightening its role in the pathophysiology of 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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".