The Effect of Statin on Anemia in Patients with Chronic Kidney Disease and End-Stage Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Although erythropoietin-stimulating agents are effective in treating anemia in patients with end-stage kidney disease (ESKD) undergoing hemodialysis, some ESKD patients, especially those with inflammation, continue to suffer from anemia. Statin, an inhibitor of hydroxymethylglutaryl-CoA (HMG-CoA) reductase with lipid-lowering effects, may have a pleiotropic effect in reducing inflammation, and thus increase hemoglobin (Hb) level. We searched the PubMed, Embase, and Cochrane databases for relevant studies. The population of interest comprised advanced chronic kidney disease (CKD) patients and ESKD patients receiving hemodialysis with statin treatment. The included study designs were randomized control trial/cohort study/pre-post observational study, and outcomes of interest were Hb, erythropoietin resistance index (ERI) and ferritin. PRISMA 2020 guidelines were followed, and risk of bias (RoB) was assessed using the RoB 2.0 tool in randomized controlled trials, and the Newcastle-Ottawa scale (NOS) in cohort studies. We eventually included ten studies (5258 participants), comprising three randomized controlled trials and seven cohort studies. Overall, Hb increased by 0.84 g/dL (95% confidence interval [CI]: -0.02 to 1.70) in all groups using statins, including single-arm cohorts, and by 0.72 g/dL (95% CI: -0.02 to 1.46) in studies with placebo control. Hb levels were higher in the study group than in the control group, with a mean difference of 0.18 g/dL (95% CI: 0.04-0.32) at baseline and 1.0 g/dL (95% CI: 0.13-1.87) at the endpoint. Ferritin increased by 9.97 ng/mL (95% CI: -5.36 to 25.29) in the study group and decreased by 34.01 ng/mL (95% CI: -148.16 to 80.14) in the control group; ferritin fluctuation was higher in the control group. In conclusion, statin may improve renal anemia in ESKD patients receiving hemodialysis and regular erythropoietin-stimulating agents. Future studies with more rigorous methodology and larger sample size study should be performed to confirm this beneficial effect.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".