Evidence map›Paper›PMID 38765135›Full record

ArticleHeliyon2024

Identification of endophenotypes supporting outcome prediction in hemodialysis patients based on mechanistic markers of statin treatment.

Johannes Leierer, Madonna Salib, Michail Evgeniou, Patrick Rossignol, Ziad A Massy, Klaus Kratochwill, Gert Mayer, Bengt Fellström, Nicolas Girerd, Faiez Zannad and 1 more

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Johannes LeiererMedical University of Innsbruck, Department of Internal Medicine IV, Innsbruck, Austria.
Madonna SalibUniversité de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France.
Michail EvgeniouMedical University of Vienna, Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Division of Pediatric Nephrology and Gastroenterology, Vienna, Austria.
Patrick RossignolUniversité de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France.
Ziad A MassyAssociation pour l'Utilisation du Rein Artificiel (AURA) Paris and Department of Nephrology, CHU Ambroise Paré, APHP, 92104, Boulogne Billancourt, and Centre for Research in Epidemiology and Population Health (CESP), University Paris-Saclay, University Versailles-Saint Quentin, Inserm UMRS, 1018, Clinical Epidemiology Team, Villejuif, France.
Klaus KratochwillMedical University of Vienna, Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Division of Pediatric Nephrology and Gastroenterology, Vienna, Austria.
Gert MayerMedical University of Innsbruck, Department of Internal Medicine IV, Innsbruck, Austria.
Bengt FellströmUppsala University, Department of Medical Sciences, Uppsala, Sweden.
Nicolas GirerdUniversité de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France.
Faiez ZannadUniversité de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France.
Paul PercoMedical University of Innsbruck, Department of Internal Medicine IV, Innsbruck, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Statins are widely used to reduce the risk of cardiovascular disease (CVD). Patients with end-stage renal disease (ESRD) on hemodialysis have significantly increased risk of developing CVD. Statin treatment in these patients however did not show a statistically significant benefit in large trials on a patient cohort level. Methods: We generated gene expression profiles for statins to investigate the impact on cellular programs in human renal proximal tubular cells and mesangial cells in-vitro. We subsequently selected biomarkers from key statin-affected molecular pathways and assessed these biomarkers in plasma samples from the AURORA cohort, a double-blind, randomized, multi-center study of patients on hemodialysis or hemofiltration that have been treated with rosuvastatin. Patient clusters (phenotypes) were created based on the identified biomarkers using Latent Class Model clustering and the associations with outcome for the generated phenotypes were assessed using Cox proportional hazards regression models. The multivariable models were adjusted for clinical and biological covariates based on previously published data in AURORA. Results: The impact of statin treatment on mesangial cells was larger as compared with tubular cells with a large overlap of differentially expressed genes identified for atorvastatin and rosuvastatin indicating a predominant drug class effect. Affected molecular pathways included TGFB-, TNF-, and MAPK-signaling and focal adhesion among others. Four patient clusters were identified based on the baseline plasma concentrations of the eight biomarkers. Phenotype 1 was characterized by low to medium levels of the hepatocyte growth factor (HGF) and high levels of interleukin 6 (IL6) or matrix metalloproteinase 2 (MMP2) and it was significantly associated with outcome showing increased risk of developing major adverse cardiovascular events (MACE) or cardiovascular death. Phenotype 2 had high HGF but low Fas cell surface death receptor (FAS) levels and it was associated with significantly better outcome at 1 year. Conclusions: In this translational study, we identified patient subgroups based on mechanistic markers of statin therapy that are associated with disease outcome in patients on hemodialysis.

Indexed as

AURORA cohortCardiovascular riskCox proportional hazards regression modelsGene expression profilingHemodialysisPatient stratificationPredictive biomarkersStatins

Identifiers

PMID38765135
PMCPMC11098839

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.