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Summary: Background: 1H-NMR metabolomics and DNA methylation in blood are widely known biomarkers predicting age-related physiological decline and mortality yet exert mutually independent mortality and frailty signals. Methods: Leveraging multi-omics data in four Dutch population studies (N = 5238, ∼40% of which male) we investigated whether the mortality signal captured by 1H-NMR metabolomics could guide the construction of DNA methylation-based mortality predictors. Findings: We trained DNA methylation-based surrogates for 64 metabolomic analytes and found that analytes marking inflammation, fluid balance, or HDL/VLDL metabolism could be accurately reconstructed using DNA-methylation assays. Interestingly, a previously reported multi-analyte score indicating mortality risk (MetaboHealth) could also be accurately reconstructed. Sixteen of our derived surrogates, including the MetaboHealth surrogate, showed significant associations with mortality, independent of relevant covariates. Interpretation: The addition of our metabolic analyte-derived surrogates to the well-established epigenetic clock GrimAge demonstrates that our surrogates potentially represent valuable mortality signal. Funding: BBMRI-NL, X-omics, VOILA, Medical Delta, NWO, ERC.

Aims: Cardiac manifestations are common in COVID-19, often elevated serum troponin levels or myocardial dysfunction on trans-thoracic echocardiography (TTE) is observed. Both parameters are associated with increased in-hospital mortality. Possibly, subclinical coronary atherosclerosis plays a role, of which severity can be assessed by calculating the coronary artery calcium (CAC) score. This study aims to determine the relation between coronary atherosclerosis and cardiac manifestations in COVID-19 survivors. Methods: This study was conducted at the Leiden University Medical Center. All patients admitted for COVID-19 were included and scheduled for a 6-week follow-up visit with trans-thoracic echocardiography (TTE). CAC was assessed according to an ordinal score on non-gated, non-contrast enhanced computed tomography of the chest. Patients with and without CAC were compared on cardiac injury as reflected by elevated serum troponin levels and impaired cardiac function assessed through TTE. Results: In total, 146 patients were included. Mean age was 62 years and 62 % of the patients were male. During admission, patients with CAC showed significantly higher levels of troponin (19 ng/L vs 10 ng/L; p

Chest computed tomography (CT) is not routinely used for the diagnosis of myocardial infarction. However, there have been some reports of patients undergoing chest CT for other indications in which ultimately MI was diagnosed due to the presence of areas of myocardial hypoperfusion. The authors present the case of a 60-years old male who is referred due to acute chest pain radiating between the scapulae. Thoracic computed tomography angiography to rule out an aortic dissection revealed an occlusion of the left anterior descending coronary artery with an area of relative hypoperfusion. Hence, the present case demonstrates how using routine thoracic computed tomography angiography, transmural ischemia can be visualized in the setting of an acute myocardial infarction. It should make clinicians aware of the fact it may be beneficial to look for myocardial perfusion abnormalities when assessing chest CT's.

Purpose: This study aims to investigate the correlation between myocardial area at risk at coronary computed tomography angiography (CCTA) and the ischemic burden derived from myocardial computed tomography perfusion (CTP) by using the 17-segment model. Methods: Forty-two patients with chest pain complaints who underwent a combined CCTA and CTP protocol were identified. Patients with reversible ischemia at CTP and at least one stenosis of ≥ 50% at CCTA were selected. Myocardial area at risk was calculated using a Voronoi-based segmentation algorithm at CCTA and was defined as the sum of all territories related to a ≥ 50% stenosis as a percentage of the total left ventricular (LV) mass. The latter was calculated using LV contours which were automatically drawn using a machine learning algorithm. Subsequently, the ischemic burden was defined as the number of segments demonstrating relative hypoperfusion as a percentage of the total amount of segments (=17). Finally, correlations were tested between the myocardial area at risk and the ischemic burden using Pearson’s correlation coefficient. Results: A total of 77 coronary lesions were assessed. Average myocardial area at risk and ischemic burden for all lesions was 59% and 23%, respectively. Correlations for ≥ 50% and ≥ 70% stenosis based myocardial area at risk compared to ischemic burden were moderate (r = 0.564; p