Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): CIBERCV of cardiovascular diseases; CIBERESP. OnBehalf REGICOR investigators Background Although myocardial infarction (MI) severity trends are systematically measured with mortality rates, MI incidence trends and the precision of its estimation by linkage of anonymized electronic databases are relatively untested. We validated a linked-data method (LDM) to estimate a population MI incidence and case-fatality, and to analyse the 1990-2016 trends in North-Eastern Spain.  Methods LDM consisted of linking MI hospital discharges (n = 4,533,981) and mortality registry data (n = 40,676) for 2008-2016, selecting key MI diagnostic codes. The prospective REGICOR study, including all MI cases in Girona, 1990-2009, was used as the gold standard for validation purposes. Standardized MI cumulated incidence and 28-day case fatality for population aged 35-74 years was calculated, 1990-2016 trends were analysed by linear and joinpoint regression and annual percentage change (APC). Results LDM and REGICOR MI incidence and case-fatality estimates were similar for 2008-09 (Table). LDM MI incidence and case-fatality significantly decreased: APC 1990-2016 [95% CI]) was -1.8 [-2.6;-0.9] in women, and APC 2002-2016  -2.8 [-3.8;-1.8] in men; case-fatality APC 1990-2016 was -4.7% [-5.7;-3.8] in women and APC 1995-2005 -6.5%[-8.5;-4.5] in men.  Conclusions LDM in population aged 35-74 reliably estimated MI incidence and case-fatality. MI incidence and case-fatality significantly decreased after 1990. Comparative analysis of REGICOR vs LDM REGICOR 2008-2009 LDM 2008-2009 Estimate 95% CI Estimate 95% CI P-Value Cumulated incidence Men 245.4 228.3; 262.5 239.7 222.5; 256.3 0.626 Women 61.1 52.6; 69.6 58.2 49.9; 66.4 0.626 Overall 28-day case-fatality Men 23.5% 19.7; 27.2 21.3% 17.8; 24.9 0.422 Women 19.3% 6.7; 31.9 17.7% 6.3; 29.1 0.855 In-hospital case-fatality Men 6.9% 4.8; 9.0 5.7% 3.8; 7.6 0.394 Women 5.0% 1.9; 8.2 3.7% 1.0; 6.5 0.540 Pre-hospital case-fatality Men 16.5% 11.8; 21.3 15.6% 11.2; 20.1 0.791 Women 14.2 % 6.6; 21.9 14.0% 6.3; 21.7 0.961 CI Confidence Interval

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute Agency for Management of University and Research Grants Background Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, discrimination of CV risk functions is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there is no data of CV risk functions including time-dependent covariates, competing risks and treatments. Aim To examine the effect of including time-dependent covariates, competing risks and treatments in CV risk prediction. Methods Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35-74 years without previous history of cardiovascular disease were included (n=8,470). Coronary and stroke events, and mortality due to other CV causes or to cancer were recorded during the follow-up (median=12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with the basal measurement of classical risk factors, treatments or competing risks. Models were cross-validated and compared by their discrimination (area under the ROC curve), calibration (Hosmer-Lemeshow test) and reclassification (categorical net reclassification index). Results Cancer mortality was the event with the highest cumulative incidence. In coronary event prediction, cholesterol and hypertension treatment addition to classical risk factors, improved significantly discrimination by 2% and reclassification by 7-9%. In stroke event prediction, inclusion of time-dependent covariates decreased significantly discrimination by 3-5%. Conclusion Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary/stroke prediction does not improve with 2 measurements of covariates or with competing risks.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute and the European Regional Development FundAgency for Management of University and Research Grants Introduction In the next decades, it is expected an increasing incidence of acute coronary syndrome in the Spanish population. Identify high-risk patients could be the most cost-effective way to reduce its incidence and morbi-mortality burden. We studied the effect of adding coronary artery calcification (CAC) and the extent of atherosclerotic disease (SIS score) to classical cardiovascular (CV) risk factors in coronary risk prediction. Methods This was a prospective cohort study of 325 asymptomatic patients recruited between 2013-2017. Demographic characteristics and CV risk factors were obtained and all participants underwent a coronary computed tomography angiography exam in which CAC and SIS were determined. The cohort was followed-up (median= 4 years) for a composite endpoint that included CV death, myocardial infarction, coronary angiography and/or revascularization. Improvement in discrimination and in reclassification by the inclusion of CAC/SIS in the Framingham-REGICOR function were examined with the Sommer's D index and with the Net reclassification index (NRI) (categorical and continuous), respectively. Results Nine of the 251 individuals included in the study had an event in the follow-up. Of the included participants, 94 had a CAC = 0 and 85 a SIS = 0. These participants had no events. The addition of SIS or of SIS and CAC scores to the Framigham-REGICOR risk function increased significantly the discrimination capacity from 0.71 to 0.88 (Table 1). Reclassification measured by the continuous NRI also improved significantly from 69.5 to 112.4/115.5 when SIS or both scores were included (Table 2). Conclusions CAC and SIS scores were associated to 4-year CV event incidence, independently of coronary risk estimation. Discrimination and reclassification of the Framigham-REGICOR coronary function were significantly improved by both indexes, but SIS overrode the effect of CAC.

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Aims: Current risk prediction tools are not accurate enough to identify most individuals at high coronary risk. On the other hand, oxidized low-density lipoproteins (ox-LDLs) and miRNAs are actively involved in atherosclerosis. Our aim was to examine the association of ox-LDL-induced miRNAs with coronary artery disease (CAD), and to assess their predictive capacity of future CAD. Methods and results: Human endothelial and vascular smooth muscle cells were treated with oxidized or native LDLs (nLDL), and their miRNA expression was measured with the miRNA 4.0 array, and analyzed with moderated t-tests. Differently expressed miRNAs and others known to be associated with CAD, were examined in serum samples of 500 acute myocardial infarction (AMI) patients and 500 healthy controls, and baseline serum of 117 incident CAD cases and c 485 randomly-selected cohort participants (case-cohort). Both were developed within the REGICOR AMI Registry and population cohorts from Girona. miRNAs expression in serum was measured with custom OpenArray plates, and analyzed with fold change (age and sex-paired case-control) and survival models (case-cohort). Improvement in discrimination and reclassification by miRNAs was assessed. Twenty-one miRNAs were up- or down-regulated with ox-LDL in cell cultures. One of them, 1 (has-miR-122-5p, fold change=4.85) was upregulated in AMI cases. Of the 28 known CAD-associated miRNAs, 11 were upregulated in AMI cases, and 1 (hsa-miR-143-3p, hazard ratio=0.56 [0.38-0.82]) was associated with CAD incidence and improved reclassification. Conclusion: We identified 2 novel miRNAs associated with ox-LDLs (hsa-miR-193b-5p and hsa-miR-1229-5p), and 1 miRNA that improved reclassification of healthy individuals (hsa-miR-143-3p).