1. Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Hannover, Germany.
2. Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.
3. CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.
4. Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
5. REBIRTH Center for Translational Regenerative Medicine, Hannover Medical School, Hannover, Germany.
6. Université de Lorraine, Inserm, Centre d'Investigations cliniques-plurithématique 1433, Inserm U1116; CHRU Nancy; F-CRIN INI-CRCT network, Nancy, France.
7. Department of Medical Sciences, Renal Unit, Uppsala University Hospital, Uppsala, Sweden.
8. Department of Nephrology and Hypertension, University Hospital, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany.
9. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
10. Division of Nephrology, Ambroise Paré University Medical Center, APHP, Boulogne Billancourt, F-92100 Paris, France.
11. INSERM U1018, Team 5, CESP (Centre de Recherche en Épidémiologie et Santé des Populations), Paris-Saclay University, Paris-Sud University and Paris Ouest-Versailles-Saint-Quentin-en-Yvelines University (UVSQ), F-94800 Villejuif, France.
12. Department of Transplantation Medicine, Rikshospitalet, Oslo University Hospital, Oslo, Norway.
* These authors contributed equally.
Rationale: To test whether novel biomarkers, such as microribonucleic acids (miRNAs), and nonstandard predictive models, such as decision tree learning, provide useful information for medical decision-making in patients on hemodialysis (HD).
Methods: Samples from patients with end-stage renal disease receiving HD included in the AURORA trial were investigated (n=810). The study included two independent phases: phase I (matched cases and controls, n=410) and phase II (unmatched cases and controls, n=400). The composite endpoint was cardiovascular death, nonfatal myocardial infarction or nonfatal stroke. miRNA quantification was performed using miRNA sequencing and RT-qPCR. The CART algorithm was used to construct regression tree models. A bagging-based procedure was used for validation.
Results: In phase I, miRNA sequencing in a subset of samples (n=20) revealed miR-632 as a candidate (fold change=2.9). miR-632 was associated with the endpoint, even after adjusting for confounding factors (HR from 1.43 to 1.53). These findings were not reproduced in phase II. Regression tree models identified eight patient subgroups with specific risk patterns. miR-186-5p and miR-632 entered the tree by redefining two risk groups: patients older than 64 years and with hsCRP<0.827 mg/L and diabetic patients younger than 64 years. miRNAs improved the discrimination accuracy at the beginning of the follow-up (24 months) compared to the models without miRNAs (integrated AUC [iAUC]=0.71).
Conclusions: The circulating miRNA profile complements conventional risk factors to identify specific cardiovascular risk patterns among patients receiving maintenance HD.
Keywords: Biomarker, Cardiovascular risk, Hemodialysis, Kidney disease, Machine learning, microRNA.