microRNA neural networks improve diagnosis of acute coronary syndrome (ACS).

BACKGROUND: Cardiac troponins are the preferred biomarkers of acute myocardial infarction. Despite superior sensitivity, serial testing of Troponins to identify patients suffering acute coronary syndromes is still required in many cases to overcome limited specificity. Moreover, unstable angina pectoris relies on reported symptoms in the troponin-negative group. In this study, we investigated genome-wide miRNA levels in a prospective cohort of patients with clinically suspected ACS and determined their diagnostic value by applying an in silico neural network. METHODS: PAXgene blood and serum samples were drawn and hsTnT was measured in patients at initial presentation to our Chest-Pain Unit. After clinical and diagnostic workup, patients were adjudicated by senior cardiologists in duty to their final diagnosis: STEMI, NSTEMI, unstable angina pectoris and non-ACS patients. ACS patients and a cohort of healthy controls underwent deep transcriptome sequencing. Machine learning was implemented to construct diagnostic miRNA classifiers. RESULTS: We developed a neural network model which incorporates 34 validated ACS miRNAs, showing excellent classification results. By further developing additional machine learning models and selecting the best miRNAs, we achieved an accuracy of 0.96 (95% CI 0.96-0.97), sensitivity of 0.95, specificity of 0.96 and AUC of 0.99. The one-point hsTnT value reached an accuracy of 0.89, sensitivity of 0.82, specificity of 0.96, and AUC of 0.96. CONCLUSIONS: Here we show the concept of neural network based biomarkers for ACS. This approach also opens the possibility to include multi-modal data points to further increase precision and perform classification of other ACS differential diagnoses.

Clinical genetics and outcome of left ventricular non-compaction cardiomyopathy.

AIMS: In this study, we aimed to clinically and genetically characterize LVNC patients and investigate the prevalence of variants in known and novel LVNC disease genes. INTRODUCTION: Left ventricular non-compaction cardiomyopathy (LVNC) is an increasingly recognized cause of heart failure, arrhythmia, thromboembolism, and sudden cardiac death. We sought here to dissect its genetic causes, phenotypic presentation and outcome. METHODS AND RESULTS: In our registry with follow-up of in the median 61 months, we analysed 95 LVNC patients (68 unrelated index patients and 27 affected relatives; definite familial LVNC = 23.5%) by cardiac phenotyping, molecular biomarkers and exome sequencing. Cardiovascular events were significantly more frequent in LVNC patients compared with an age-matched group of patients with non-ischaemic dilated cardiomyopathy (hazard ratio = 2.481, P = 0.002). Stringent genetic classification according to ACMG guidelines revealed that TTN, LMNA, and MYBPC3 are the most prevalent disease genes (13 patients are carrying a pathogenic truncating TTN variant, odds ratio = 40.7, Confidence interval = 21.6-76.6, P < 0.0001, percent spliced in 76-100%). We also identified novel candidate genes for LVNC. For RBM20, we were able to perform detailed familial, molecular and functional studies. We show that the novel variant p.R634L in the RS domain of RBM20 co-segregates with LVNC, leading to titin mis-splicing as revealed by RNA sequencing of heart tissue in mutation carriers, protein analysis, and functional splice-reporter assays. CONCLUSION: Our data demonstrate that the clinical course of symptomatic LVNC can be severe. The identified pathogenic variants and distribution of disease genes-a titin-related pathomechanism is found in every fourth patient-should be considered in genetic counselling of patients. Pathogenic variants in the nuclear proteins Lamin A/C and RBM20 were associated with worse outcome.