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Theranostics 2020; 10(19):8665-8676. doi:10.7150/thno.46123 This issue Cite

Research Paper

Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids

David de Gonzalo-Calvo1,2,3*, Pablo Martínez-Camblor4*, Christian Bär1,5*, Kevin Duarte6, Nicolas Girerd6, Bengt Fellström7, Roland E. Schmieder8, Alan G. Jardine9, Ziad A. Massy10,11, Hallvard Holdaas12, Patrick Rossignol6, Faiez Zannad6✉, Thomas Thum1,5✉

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.

✉ Corresponding author: Thomas Thum, MD, PhD Hannover Medical School, Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Carl-Neuberg-Str. 1, 30625 Hannover, Germany, Telephone: +49 (0) 511 532 5272 Fax: +49 (0) 511 532 5274 E-ma More

Citation:
de Gonzalo-Calvo D, Martínez-Camblor P, Bär C, Duarte K, Girerd N, Fellström B, Schmieder RE, Jardine AG, Massy ZA, Holdaas H, Rossignol P, Zannad F, Thum T. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics 2020; 10(19):8665-8676. doi:10.7150/thno.46123. https://www.thno.org/v10p8665.htm
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Abstract

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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.

Citation styles

APA
de Gonzalo-Calvo, D., Martínez-Camblor, P., Bär, C., Duarte, K., Girerd, N., Fellström, B., Schmieder, R.E., Jardine, A.G., Massy, Z.A., Holdaas, H., Rossignol, P., Zannad, F., Thum, T. (2020). Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics, 10(19), 8665-8676. https://doi.org/10.7150/thno.46123.

ACS
de Gonzalo-Calvo, D.; Martínez-Camblor, P.; Bär, C.; Duarte, K.; Girerd, N.; Fellström, B.; Schmieder, R.E.; Jardine, A.G.; Massy, Z.A.; Holdaas, H.; Rossignol, P.; Zannad, F.; Thum, T. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics 2020, 10 (19), 8665-8676. DOI: 10.7150/thno.46123.

NLM
de Gonzalo-Calvo D, Martínez-Camblor P, Bär C, Duarte K, Girerd N, Fellström B, Schmieder RE, Jardine AG, Massy ZA, Holdaas H, Rossignol P, Zannad F, Thum T. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics 2020; 10(19):8665-8676. doi:10.7150/thno.46123. https://www.thno.org/v10p8665.htm

CSE
de Gonzalo-Calvo D, Martínez-Camblor P, Bär C, Duarte K, Girerd N, Fellström B, Schmieder RE, Jardine AG, Massy ZA, Holdaas H, Rossignol P, Zannad F, Thum T. 2020. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics. 10(19):8665-8676.

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