Background: Dilated cardiomyopathy (DCM) is a cardiac condition characterized by unexplained heart failure. More than 100 DCM genes have been reported of which only one gene (RBM20) has regulatory properties. Purpose: To investigate the frequency of RBM20 mutations in Danish DCM patients and compare the sex specific disease expression. Methods: The cohort consisted of 112 consecutive DCM index-patients who underwent genetic investigations of 67 DCM genes. All relatives at risk of disease development were invited for clinical investigations including ECG-recording and echocardiography. When a disease-associated mutation was identified in a indexpatient, relatives were offered genetic cascade screening. Statistical analyses were made by use of T-, χ2or Fishers exacttest. Results (see table 1): Nine families were identified with 4 different recognized RBM20 mutations. The frequency of RBM20 mutations was 8% (9/112). Cascade screening identified a total of 69 mutation carriers. Males were diagnosed at an earlier age (p<0.001) with a significantly larger left ventricular end diastolic diameter (LVEDd) (p=0.001) and a lower left ventricular ejection fraction (LVEF) (p=0.016) than female carriers. Eight males and no females underwent heart transplantation (HTx) (p=0.037).

Background: Due to a number of common proteins expressed both in cardiac and skeletal muscles, all types of cardiomyopathies are associated with neuromuscular phenotype.Thus, mutations in LMNA, DES or FLNC, and TTN genes could be found in dilated, restrictive, or hypertrophic cardiomyopathy often in combination with conduction disorders, myopathy, or myodystrophy.The genetic spectrum of cardiomyopathies associated with neuromuscular disorders is increasing, therefore giving an additional knowledge of molecular pathogenesis of cardiomyopathies.In the present study, we applied an exome sequencing strategy to identify a new genetic causes of cventricardiomyopathies and myopathies. Materials and methods:The study was approved by the Institute Ethical Review Board.Written informed consent was obtained from all subjects and their representatives prior to investigation.The genotyping was performed using Halo-Plex target enrichment panel followed by Sure Select Exome sequencing in genotype-negative patients.The identified genetic variants were classified according to ACMG guidelines.Satellite cells were isolated enzymatically from m. soleus biopsy.Immunocytochemistry and Western blot analysis were performed according to standard protocols.Results: 17 patients with cardiomyopathy and neuromuscular phenotype were included into the study.In all patients, cardiac manifestation of the disorder (dilated, restrictive, hypertrophic, or arrhythmogenic cardiomyopathy) preceded the diagnosis of neuromuscular disorder.In 16 patients, the mutation in earlier described cardiomyopathy and myopathy-associated genes such as DYS, DES, LMNA, FLNC, and SYNE1 were identified.In one patient, a mutation in a newly described MYOF (2615delG:p.G872fs) was identified by exome sequencing.The patient presented with recurrent ventricular tachycardia (8 000 per day), frequent ventricular extrasystoles, and moderate left ventricular dilation with preserved systolic and diastolic function at 43 years old.Routine clinical testing revealed marked CK elevation (2 500 U/L) and detailed neurological examination showed limb girdle muscle weakness more prominent in the low extremities.Myopathic pattern was confirmed by electromyography and morphological examination of muscle biopsy confirmed myodystrophic features.Staining of patient's muscle satellite cells with anti-MYOF antibody showed weakened and moderately disorganized pattern compared to control cells.Western blot analysis performed on patient's satellite cells demonstrated 50% reduction of MYOF content compared to donor cells.According to ACMG guidelines, the variant was classified as pathogenic (PVS1, PS1, PM1). Conclusion:In the present study, we demonstrated for the first time the association of MYOF gene with the development of cardiac and skeletal muscle disorders.More detailed functional and morphological studies are needed to evolve the role of MYOF in cardiac myocyte function.

Y.C Ashkenazy, M. Lewkowicz, J. Levitan, S. Havlin, K. Saermark, H. Moelgaard and P.E. Bloch Thomsen (a) Dept. of Physics, Bar-Ilan University, Ramat-Gan, Israel (b) Gonda Goldschmied Center, Bar-Ilan University, Ramat-Gan, Israel (c) Dept. of Physics, College of Judea and Samaria, Ariel, Israel (d) Dept. of Physics, The Technical University of Denmark, Lyngby, Denmark (e) Dept. of Cardiology, Skejby Sygehus, Aarhus University Hospital, Aarhus, Denmark (f) Dept of Cardiology, Amtssygehuset i Gentofte, Copenhagen University Hospital, Denmark (September 2, 2004)

We study the correlation properties of heartbeat fluctuations using scale-specific variance (root-mean-square fluctuation) and scaling (correlation) exponents as measures of healthy and cardiac impaired individuals. Our results show that the variance and the scaling exponent are uncorrelated. We find that the variance measure at certain scales is well suited to separate healthy subjects from heart patients. However, for mortality prediction the scaling exponents outperform the variance measure. Our study is based on a database containing recordings from 428 individuals after myocardial infarct (MI) and on a database containing 105 healthy subjects and 11 heart patients. The results have been obtained by applying two recently developed methods (DFA -Detrended Fluctuation Analysis and WAV -Multiresolution Wavelet Analysis) which are shown to be highly correlated.

We present a probability analysis of RR interval series. The analysis is based on a subdivision of the RR interval series into segments — here called m-segments — ranging from one local RR maximum value to the consecutively following local RR maximum value; such segments represent full cycles of acceleration followed by deceleration of the heart rate. We analyze the properties of m-segments in terms of the probability of observing j RR values in the segment and in terms of the probability of observing a change of Δj in going from one m-segment to the following m-segment. We also calculate the rms-values, σm, of the m-segments and introduce an entropy measure to characterize the RR interval series. Further, we present a simple model for phase-space-trajectory plots for RR interval series and an estimate of the spectral properties in terms of the parameters j and σm. The results are based on 24-hour RR interval series for ten heart transplant patients, with a total of 23 recordings, for 326 myocardial infarction patients and 104 healthy subjects. The results obtained show characteristic differences between the behavior of the three groups, but no attempt at a clinical interpretation is made.

We compare three recently applied methods for analyzing heart rate variability: detrended fluctuation analysis (DFA), multiresolution wavelet analysis (WAV) and detrended time series analysis (DTS). In the comparison, both scale-dependent and scale-independent measures are considered. In agreement with recent results by Thurner et al.,(9) we conclude that scale-dependent measures are well suited to separate healthy subjects from patients with heart disease. However, as regards the use in Kaplan-Meier cumulative survival curves, scale-independent measures (generally slope values) clearly outperform scale dependent measures (generally rms values). The comparison is mainly based on a database containing recordings from 428 patients with heart disease (myocardial infarct) and on a database containing 105 healthy subjects and 11 heart patients.

Multiresolution Wavelet Transform and Detrended Fluctuation Analysis have recently been proven to be excellent methods in the analysis of Heart Rate Variability and in distinguishing between healthy subjects and patients with various dysfunctions of the cardiac nervous system. We argue that it is possible to obtain a distinction between healthy subjects/patients of at least similar quality by, first, detrending the time-series of RR-intervals by subtracting a running average based on a local window with a length of around 32 data points, then calculating the standard deviation of the detrended time-series. The results presented here indicate that the analysis can be based on very short time-series of RR-data (7–8 minutes), which is a considerable improvement relative to 24-hour Holter recordings.

We demonstrate that it is possible to distinguish with a complete certainty between healthy subjects and patients with various dysfunctions of the cardiac nervous system by way of multiresolutional wavelet transform of RR intervals. We repeated the study of Thurner et al. on different ensemble of subjects. We show that reconstructed series using a filter which discards wavelet coefficient related with higher scales enables one to classify individuals for which the method otherwise is inconclusive. We suggest a delimiting diagnostic value of the standard deviation of the filtered, reconstructed RR interval time series in the range of ~ 0.035 (for the above mentioned filter), below which individuals are at risk.