Torbjørn Velle-Forbord1, Maria Eidlaug1, Julia Debik1, Julie Caroline Sæther2, Turid Follestad3, Javaid Nauman4, Bruna Gigante5, Helge Røsjø6, Torbjørn Omland6, Mette Langaas7, Anja Bye8. 1. Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway. 2. Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway; Department of Cardiology, St. Olavs Hospital, Trondheim, Norway. 3. Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway. 4. Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway; Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates. 5. Unit of Cardiovascular Epidemiology, Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 6. Division of Medicine, Akershus University Hospital, Lørenskog, Norway; Division of Medicine and Laboratory Sciences, The University of Oslo, Oslo, Norway. 7. Department of Mathematical Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Norway. 8. Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway; Department of Cardiology, St. Olavs Hospital, Trondheim, Norway. Electronic address: Anja.Bye@ntnu.no.
Abstract
BACKGROUND AND AIMS: Several risk prediction models for coronary heart disease (CHD) are available today, however, they only explain a modest proportion of the incidence. Circulating microRNAs (miRs) have recently been associated with processes in CHD development, and may therefore represent new potential risk markers. The aim of the study was to assess the incremental value of adding circulating miRs to the Framingham Risk Score (FRS). METHODS: This is a case-control study with a 10-year observation period, with fatal and non-fatal myocardial infarction (MI) as endpoint. At baseline, ten candidate miRs were quantified by real-time polymerase chain reaction in serum samples from 195 healthy participants (60-79 years old). During the follow-up, 96 participants experienced either a fatal (n = 36) or a non-fatal MI (n = 60), whereas the controls (n = 99) remained healthy. By using best subset logistic regression, we identified the miRs that together with the FRS for hard CHD best predicted future MI. The model evaluation was performed by 10-fold cross-validation reporting area under curve (AUC) from the receiver operating characteristic curve (ROC). RESULTS: The best miR-based logistic regression risk-prediction model for MI consisted of a combination of miR-21-5p, miR-26a-5p, mir-29c-3p, miR-144-3p and miR-151a-5p. By adding these 5 miRs to the FRS, AUC increased from 0.66 to 0.80. In comparison, adding other important CHD risk factors (waist-hip ratio, triglycerides, glucose, creatinine) to the FRS only increased AUC from 0.66 to 0.68. CONCLUSIONS: Circulating levels of miRs can add value on top of traditional risk markers in predicting future MI in healthy individuals.
BACKGROUND AND AIMS: Several risk prediction models for coronary heart disease (CHD) are available today, however, they only explain a modest proportion of the incidence. Circulating microRNAs (miRs) have recently been associated with processes in CHD development, and may therefore represent new potential risk markers. The aim of the study was to assess the incremental value of adding circulating miRs to the Framingham Risk Score (FRS). METHODS: This is a case-control study with a 10-year observation period, with fatal and non-fatal myocardial infarction (MI) as endpoint. At baseline, ten candidate miRs were quantified by real-time polymerase chain reaction in serum samples from 195 healthy participants (60-79 years old). During the follow-up, 96 participants experienced either a fatal (n = 36) or a non-fatal MI (n = 60), whereas the controls (n = 99) remained healthy. By using best subset logistic regression, we identified the miRs that together with the FRS for hard CHD best predicted future MI. The model evaluation was performed by 10-fold cross-validation reporting area under curve (AUC) from the receiver operating characteristic curve (ROC). RESULTS: The best miR-based logistic regression risk-prediction model for MI consisted of a combination of miR-21-5p, miR-26a-5p, mir-29c-3p, miR-144-3p and miR-151a-5p. By adding these 5 miRs to the FRS, AUC increased from 0.66 to 0.80. In comparison, adding other important CHD risk factors (waist-hip ratio, triglycerides, glucose, creatinine) to the FRS only increased AUC from 0.66 to 0.68. CONCLUSIONS: Circulating levels of miRs can add value on top of traditional risk markers in predicting future MI in healthy individuals.
Authors: Dina Mukushkina; Dana Aisina; Anna Pyrkova; Alma Ryskulova; Siegfried Labeit; Anatoliy Ivashchenko Journal: Front Genet Date: 2020-11-04 Impact factor: 4.599
Authors: Irene R Dégano; Anna Camps-Vilaró; Isaac Subirana; Nadia García-Mateo; Pilar Cidad; Dani Muñoz-Aguayo; Eulàlia Puigdecanet; Lara Nonell; Joan Vila; Felipe M Crepaldi; David de Gonzalo-Calvo; Vicenta Llorente-Cortés; María Teresa Pérez-García; Roberto Elosua; Montserrat Fitó; Jaume Marrugat Journal: J Clin Med Date: 2020-05-09 Impact factor: 4.241