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Circulating microRNAs as predictive biomarkers of myocardial infarction: Evidence from the HUNT study

  • Author Footnotes
    1 These authors contributed equally to this work.
    Torbjørn Velle-Forbord
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
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  • Author Footnotes
    1 These authors contributed equally to this work.
    Maria Eidlaug
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
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  • Julia Debik
    Affiliations
    Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
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  • Julie Caroline Sæther
    Affiliations
    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
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  • Turid Follestad
    Affiliations
    Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
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  • Javaid Nauman
    Affiliations
    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
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  • Bruna Gigante
    Affiliations
    Unit of Cardiovascular Epidemiology, Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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  • Helge Røsjø
    Affiliations
    Division of Medicine, Akershus University Hospital, Lørenskog, Norway

    Division of Medicine and Laboratory Sciences, The University of Oslo, Oslo, Norway
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  • Torbjørn Omland
    Affiliations
    Division of Medicine, Akershus University Hospital, Lørenskog, Norway

    Division of Medicine and Laboratory Sciences, The University of Oslo, Oslo, Norway
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  • Mette Langaas
    Affiliations
    Department of Mathematical Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Norway
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  • Anja Bye
    Correspondence
    Corresponding author. NTNU, Department of Circulation and Medical Imaging, Postboks 8905, Medisinsk teknisk forskningssenter, 7491, Trondheim, Norway.
    Affiliations
    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
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  • Author Footnotes
    1 These authors contributed equally to this work.

      Highlights

      • 6 circulating microRNAs were associated with 10-year risk of myocardial infarction.
      • Adding 5 microRNAs to the Framingham Risk Score significantly improved prediction.
      • Adding other risk factors to the Framingham Risk Score did not improve prediction.

      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.

      Graphical abstract

      Keywords

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