Circulating miR-133a-3p defines a low-risk subphenotype in patients with heart failure and central sleep apnea: a decision tree machine learning approach

Journal of Translational Medicine

Research

Pablo Martinez-Camblor¹  Josephine Labus²  Christiane E. Angermann³  Martin R. Cowie⁴  Wolfgang Koenig⁵  Kevin Duarte⁶  Isabelle Riedel⁷  Andrea Korte⁷  Thomas Thum⁸  Christian Bär⁸  Thalia Belmonte⁹  Ferran Barbé⁹  David de Gonzalo-Calvo⁹  Faiez Zannad^1,10 

[1] Anesthesiology Department, Geisel School of Medicine at Dartmouth, Hanover, NH, USA;Faculty of Health Sciences, Universidad Autonoma de Chile, Providencia, Chile;Cellular Neurophysiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany;Comprehensive Heart Failure Center, University and University Hospital Würzburg, Würzburg, Germany;Department of Medicine I, University Hospital Würzburg, Würzburg, Germany;Department of Cardiology, Royal Brompton Hospital (Guy’s & St Thomas’s NHS Foundation Trust), London, UK;Deutsches Herzzentrum München, Technische Universität München, Munich, Germany;German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany;Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany;INSERM 1433, CHRU de Nancy, Centre d’Investigations Cliniques Plurithématique, Institut Lorrain du Cœur et des Vaisseaux, Université de Lorraine, Nancy, France;Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany;Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany;Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Nikolai-Fuchs-Str. 1, 30625, Hannover, Germany;Translational Research in Respiratory Medicine, IRBLleida, University Hospital Arnau de Vilanova and Santa Maria, Lleida, Spain;CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain;Université de Lorraine, Inserm, Centre d’Investigations Cliniques-Plurithématique 1433, Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT Network, Nancy, France;

关键词: Biomarker;    Central sleep apnea;    Decision tree learning;    Heart failure;    Machine learning;    microRNA;    Reduced ejection fraction;    SERVE-HF;   

DOI  :  10.1186/s12967-023-04558-w

 received in 2023-04-11, accepted in 2023-09-22,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPatients with heart failure with reduced ejection fraction (HFrEF) and central sleep apnea (CSA) are at a very high risk of fatal outcomes.ObjectiveTo test whether the circulating miRNome provides additional information for risk stratification on top of clinical predictors in patients with HFrEF and CSA.MethodsThe study included patients with HFrEF and CSA from the SERVE-HF trial. A three-step protocol was applied: microRNA (miRNA) screening (n = 20), technical validation (n = 60), and biological validation (n = 587). The primary outcome was either death from any cause, lifesaving cardiovascular intervention, or unplanned hospitalization for worsening of heart failure, whatever occurred first. MiRNA quantification was performed in plasma samples using miRNA sequencing and RT-qPCR.ResultsCirculating miR-133a-3p levels were inversely associated with the primary study outcome. Nonetheless, miR-133a-3p did not improve a previously established clinical prognostic model in terms of discrimination or reclassification. A customized regression tree model constructed using the Classification and Regression Tree (CART) algorithm identified eight patient subphenotypes with specific risk patterns based on clinical and molecular characteristics. MiR-133a-3p entered the regression tree defining the group at the lowest risk; patients with log(NT-proBNP) ≤ 6 pg/mL (miR-133a-3p levels above 1.5 arbitrary units). The overall predictive capacity of suffering the event was highly stable over the follow-up (from 0.735 to 0.767).ConclusionsThe combination of clinical information, circulating miRNAs, and decision tree learning allows the identification of specific risk subphenotypes in patients with HFrEF and CSA.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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