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dc.contributor.authorVelle-Forbord, Torbjørn
dc.contributor.authorEidlaug, Maria
dc.contributor.authorDebik, Julia Barbara
dc.contributor.authorSæther, Julia Caroline
dc.contributor.authorFollestad, Turid
dc.contributor.authorNauman, Javaid
dc.contributor.authorGigante, Bruna
dc.contributor.authorRøsjø, Helge
dc.contributor.authorOmland, Torbjørn
dc.contributor.authorLangaas, Mette
dc.contributor.authorBye, Anja
dc.identifier.citationVelle-Forbord, T., Eidlaug, M., Debik, J., Sæther, J. C., Follestad, T., Nauman, J., ... & Bye, A. (2019). Circulating microRNAs as predictive biomarkers of myocardial infarction: evidence from the HUNT study. Atherosclerosis, 289, 1-7.en
dc.description.abstractAbstract 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.en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.titleCirculating microRNAs as predictive biomarkers of myocardial infarction: Evidence from the HUNT studyen
dc.typeJournal articleen
dc.typePeer revieweden

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