Conny M. A. van Ravenswaaij-Arts

Heart Rate Variability

Heart rate variability, that is, the amount of heart rate fluctuations around the mean heart rate, can be used as a mirror of the cardiorespiratory control system. It is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. The most important application of heart rate variability analysis is the surveillance of postinfarction and diabetic patients. Heart rate variability gives information about the sympathetic-parasympathetic autonomic balance and thus about the risk for sudden cardiac death in these patients. Heart rate variability measurements are easy to perform, are noninvasive, and have good reproducibility if used under standardized conditions [1, 2]. Standardized conditions are necessary because heart rate variability is influenced by factors such as respiratory rate and posture. Increasing age is associated with lower heart rate variability, which is comparable to its influence on the classical autonomic function tests [3]. In our overview, we provide a succinct description of these physiologic influences on heart rate variability as well as of methods to measure heart rate variability. The influences of cardiovascular and neurologic disorders on heart rate variability are described in greater detail. During a 4-year period, all new papers concerning the clinical applicability of heart rate variability in fetal, neonatal, and adult medicine were collected (with the help of Current Contents, ISI, Philadelphia). For this review we selected papers from this collection and, if necessary, gathered less recent but relevant papers. Physiology of Heart Rate Variability Because of continuous changes in the sympathetic-parasympathetic balance, the sinus rhythm exhibits fluctuations around the mean heart rate. Frequent small adjustments in heart rate are made by cardiovascular control mechanisms (Figure 1). This results in periodic fluctuations in heart rate. The main periodic fluctuations found are respiratory sinus arrhythmia and baroreflex-related and thermoregulation-related heart rate variability [4, 5]. Figure 1. Scheme of the cardiovascular control mechanisms responsible for the main periodic fluctuations in heart rate. Due to inspiratory inhibition of the vagal tone, the heart rate shows fluctuations with a frequency equal to the respiratory rate [6]. The inspiratory inhibition is evoked primarily by central irradiation of impulses from the medullary respiratory to the cardiovascular center. In addition, peripheral reflexes due to hemodynamic changes and thoracic stretch receptors contribute to respiratory sinus arrhythmia [4]. Fluctuations with the same frequency occur in blood pressure and are known as Traube-Hering waves [7]. Respiratory sinus arrhythmia can be abolished by atropine or vagotomy [4, 8] and is parasympathetically mediated. The so-called 10-second rhythm in heart rate originates from self-oscillation in the vasomotor part of the baroreflex loop. These intrinsic oscillations result from the negative feedback in the baroreflex [9] and are accompanied by synchronous fluctuations in blood pressure (Mayer waves) [7]. The frequency of the fluctuations is determined by the time delay of the system. They are augmented when sympathetic tone is increased [10-12] and they decrease with sympathetic or parasympathetic blockade [4, 12]. Peripheral vascular resistance exhibits intrinsic oscillations with a low frequency [13, 14]. These oscillations can be influenced by thermal skin stimulation [15] and are thought to arise from thermoregulatory peripheral blood flow adjustments. The fluctuations in peripheral vascular resistance are accompanied by fluctuations with the same frequency in blood pressure and heart rate [15] and are mediated by the sympathetic nervous system. Heart Rate Variability Measurement Heart rate variability can be assessed in two ways: by calculation of indices [16] based on statistical operations on R-R intervals (time domain analysis) or by spectral (frequency domain) analysis of an array of R-R intervals [4]. Both methods require accurate timing of R waves. The analysis can be performed on short electrocardiogram (ECG) segments (lasting from 0.5 to 5 minutes) or on 24-hour ECG recordings. Time Domain Analysis Two types of heart rate variability indices are distinguished in time domain analysis (Figure 2, top). Beat-to-beat or short-term variability (STV) indices represent fast changes in heart rate. Long-term variability (LTV) indices are slower fluctuations (fewer than 6 per minute). Both types of indices are calculated from the R-R intervals occurring in a chosen time window (usually between 0.5 and 5 minutes). An example of a simple STV index is the standard deviation (SD) of beat-to-beat R-R interval differences within the time window. Examples of LTV indices are the SD of all the R-R intervals, or the difference between the maximum and minimum R-R interval length, within the window. With calculated heart rate variability indices, respiratory sinus arrhythmia contributes to STV, and baroreflex- and thermoregulation-related heart rate variability contribute to LTV. Figure 2. Example of an adult heart rate trace. Top. Bottom. Twenty-four-hour ECG recordings are frequently used by cardiologists to calculate heart rate variability. For instance, the SD of all R-R intervals within the 24-hour recording, or the mean of the SD of R-R intervals within successive 5-minute periods, is calculated [17-19] (Table 1). These 24-hour indices of heart rate variability also encounter ultradian rhythms (that is, with a period length greater than 1 hour) in heart rate. Table 1. Heart Rate Variability as a Marker of Prognosis after Myocardial Infarction* Frequency Domain Analysis Since spectral analysis was introduced as a method to study heart rate variability [5, 20], an increasing number of investigators prefer this method to that of calculation of heart rate variability indices Figure 2, bottom). The main advantage of spectral analysis of signals is the possibility to study their frequency-specific oscillations [7, 21, 22]. Thus not only the amount of variability but also the oscillation frequency (number of heart rate fluctuations per second) can be obtained. Spectral analysis involves decomposing the series of sequential R-R intervals into a sum of sinusoidal functions of different amplitudes and frequencies by the Fourier transform algorithm. The result can be displayed (power spectrum) with the magnitude of variability as a function of frequency [23]. Thus the power spectrum reflects the amplitude of the heart rate fluctuations present at different oscillation frequencies (see Figure 2, bottom). Spectral analysis can be performed on a short-lasting heart rate trace of 0.5 minute to several minutes. The individual R-R intervals are obtained by R-wave detection. The subsequent array of R-R intervals must be free of artifacts (for example, missed or spurious R waves). To perform a Fourier function on a time-limited signal, the signal must be periodic and stationary [7]. The series of time intervals between consecutive R waves can be treated as if these intervals are equally spaced (a function of R-R interval length against R-R interval number). The Fourier transformation will then result in a spectrum with power as a function of frequency expressed in cycles per beat. The expression can be transformed in Hertz by dividing by mean R-R interval length. To obtain a real data sequence of events equally spaced in time, the sequential R-R intervals are considered as a function of time, interpolated, and subsequently sampled. To obtain a stationary signal, a detrending procedure must be performed. This can be done by subtracting a least-square polynomial approximation from the original signal or by high-pass filtering [7]. Respiratory sinus arrhythmia gives a spectral peak around the respiratory frequency, the baroreflex-related heart rate fluctuations are found as a spectral peak around 0.1 Hz in adults [4], and the thermoregulation-related fluctuations are found as a peak below 0.05 Hz (see Figure 2, bottom). Measurement Conditions Heart rate variability can be studied under spontaneous conditions or with provocation; for example, standing or head-up tilt (increase in sympathetic tone) or deep breathing at a rate of 6 breaths per minute (increase in respiration-related heart rate variability). A 24-hour Holter ECG recording is part of the routine investigations following an acute myocardial infarction. In most of the studies concerning postinfarction patients, therefore, heart rate variability has been established using these 24-hour ECG recordings. In other fields of medicine, for example, regarding diabetic autonomic neuropathy, short-lasting ECG records (ranging from 0.5 to 10 minutes) have been used to calculate spectral and nonspectral heart rate variability indices. These short-lasting measurements were nearly always performed under standardized conditions with and without autonomic nervous system stimulation (that is, tilt and deep breathing). Commercially Available Equipment Commercial devices to assess 24-hour heart rate variability are now available. The conventional tape recorders for Holter monitoring may show variations in tape speed that may cause erroneous STV results [24]. Therefore speed control is necessary with the help of a timing track, that is, simultaneously recorded, crystal-generated timing pulses. The only study that we know of that evaluates commercially available heart rate variability equipment is the study of Molgaard and colleagues [24] concerning the Pathfinder II system. This system corrects for tape speed errors and has a high accuracy of QRS detection but contains no correction for artificially long R-R intervals [24]. The effect of artificially long R-R intervals depends on the heart rate variability index used. Maturational and Physiologic Influences on Heart Rate Variability Maturity of the Autonomic Ne

Array-Based Comparative Genomic Hybridization for the Genomewide Detection of Submicroscopic Chromosomal Abnormalities

Microdeletions and microduplications, not visible by routine chromosome analysis, are a major cause of human malformation and mental retardation. Novel high-resolution, whole-genome technologies can improve the diagnostic detection rate of these small chromosomal abnormalities. Array-based comparative genomic hybridization allows such a high-resolution screening by hybridizing differentially labeled test and reference DNAs to arrays consisting of thousands of genomic clones. In this study, we tested the diagnostic capacity of this technology using approximately 3,500 flourescent in situ hybridization-verified clones selected to cover the genome with an average of 1 clone per megabase (Mb). The sensitivity and specificity of the technology were tested in normal-versus-normal control experiments and through the screening of patients with known microdeletion syndromes. Subsequently, a series of 20 cytogenetically normal patients with mental retardation and dysmorphisms suggestive of a chromosomal abnormality were analyzed. In this series, three microdeletions and two microduplications were identified and validated. Two of these genomic changes were identified also in one of the parents, indicating that these are large-scale genomic polymorphisms. Deletions and duplications as small as 1 Mb could be reliably detected by our approach. The percentage of false-positive results was reduced to a minimum by use of a dye-swap-replicate analysis, all but eliminating the need for laborious validation experiments and facilitating implementation in a routine diagnostic setting. This high-resolution assay will facilitate the identification of novel genes involved in human mental retardation and/or malformation syndromes and will provide insight into the flexibility and plasticity of the human genome.

Alterations in the ankyrin domain of TRPV4 cause congenital distal SMA, scapuloperoneal SMA and HMSN2C

Spinal muscular atrophies (SMA, also known as hereditary motor neuropathies) and hereditary motor and sensory neuropathies (HMSN) are clinically and genetically heterogeneous disorders of the peripheral nervous system. Here we report that mutations in the TRPV4 gene cause congenital distal SMA, scapuloperoneal SMA, HMSN 2C. We identified three missense substitutions (R269H, R315W and R316C) affecting the intracellular N-terminal ankyrin domain of the TRPV4 ion channel in five families. Expression of mutant TRPV4 constructs in cells from the HeLa line revealed diminished surface localization of mutant proteins. In addition, TRPV4-regulated Ca2+ influx was substantially reduced even after stimulation with 4αPDD, a TRPV4 channel-specific agonist, and with hypo-osmotic solution. In summary, we describe a new hereditary channelopathy caused by mutations in TRPV4 and present evidence that the resulting substitutions in the N-terminal ankyrin domain affect channel maturation, leading to reduced surface expression of functional TRPV4 channels.

De novo mutations in the actin genes ACTB and ACTG1 cause Baraitser-Winter syndrome

Brain malformations are individually rare but collectively common causes of developmental disabilities. Many forms of malformation occur sporadically and are associated with reduced reproductive fitness, pointing to a causative role for de novo mutations. Here, we report a study of Baraitser-Winter syndrome, a well-defined disorder characterized by distinct craniofacial features, ocular colobomata and neuronal migration defect. Using whole-exome sequencing of three proband-parent trios, we identified de novo missense changes in the cytoplasmic actin–encoding genes ACTB and ACTG1 in one and two probands, respectively. Sequencing of both genes in 15 additional affected individuals identified disease-causing mutations in all probands, including two recurrent de novo alterations (ACTB, encoding p.Arg196His, and ACTG1, encoding p.Ser155Phe). Our results confirm that trio-based exome sequencing is a powerful approach to discover genes causing sporadic developmental disorders, emphasize the overlapping roles of cytoplasmic actin proteins in development and suggest that Baraitser-Winter syndrome is the predominant phenotype associated with mutation of these two genes.

Mutation update on the CHD7 gene involved in CHARGE syndrome.

CHD7 is a member of the chromodomain helicase DNA‐binding (CHD) protein family that plays a role in transcription regulation by chromatin remodeling. Loss‐of‐function mutations in CHD7 are known to cause CHARGE syndrome, an autosomal‐dominant malformation syndrome in which several organ systems, for example, the central nervous system, eye, ear, nose, and mediastinal organs, are variably involved. In this article, we review all the currently described CHD7 variants, including 183 new pathogenic mutations found by our laboratories. In total, we compiled 528 different pathogenic CHD7 alterations from 508 previously published patients with CHARGE syndrome and 294 unpublished patients analyzed by our laboratories. The mutations are equally distributed along the coding region of CHD7 and most are nonsense or frameshift mutations. Most mutations are unique, but we identified 94 recurrent mutations, predominantly arginine to stop codon mutations. We built a locus‐specific database listing all the variants that is easily accessible at www.CHD7.org. In addition, we summarize the latest data on CHD7 expression studies, animal models, and functional studies, and we discuss the latest clinical insights into CHARGE syndrome. Hum Mutat 33:1149–1160, 2012.

Nine patients with a microdeletion 15q11.2 between breakpoints 1 and 2 of the Prader–Willi critical region, possibly associated with behavioural disturbances

Behavioural differences have been described in patients with type I deletions (between breakpoints 1 and 3 (BP1-BP3)) or type II deletions (between breakpoints 2 and 3) of the 15q11.2 Prader-Willi/Angelman region. The larger type I deletions appear to coincide with more severe behavioural problems (autism, ADHD, obsessive-compulsive disorder). The non-imprinted chromosomal segment between breakpoints 1 and 2 involves four highly conserved genes, TUBGCP5, NIPA1, NIPA2, and CYFIP1; the latter three are widely expressed in the central nervous system, while TUBGCP5 is expressed in the subthalamic nuclei. These genes might explain the more severe behavioural problems seen in type I deletions. We describe nine cases with a microdeletion at 15q11.2 between BP1-BP2, thus having a haploinsufficiency for TUBGCP5, NIPA1, NIPA2, and CYFIP1 without Prader-Willi/Angelman syndrome. The clinical significance of a pure BP1-BP2 microdeletion has been debated, however, our patients shared several clinical features, including delayed motor and speech development, dysmorphisms and behavioural problems (ADHD, autism, obsessive-compulsive behaviour). Although the deletion often appeared to be inherited from a normal or mildly affected parent, it was de novo in two cases and we did not find it in 350 healthy unrelated controls. Our results suggest a pathogenic nature for the BP1-BP2 microdeletion and, although there obviously is an incomplete penetrance, they support the existence of a novel microdeletion syndrome in 15q11.2.

Further molecular and clinical delineation of co-locating 17p13.3 microdeletions and microduplications that show distinctive phenotypes

Background Chromosome 17p13.3 contains extensive repetitive sequences and is a recognised region of genomic instability. Haploinsufficiency of PAFAH1B1 (encoding LIS1) causes either isolated lissencephaly sequence or Miller–Dieker syndrome, depending on the size of the deletion. More recently, both microdeletions and microduplications mapping to the Miller–Dieker syndrome telomeric critical region have been identified and associated with distinct but overlapping phenotypes. Methods Genome-wide microarray screening was performed on 7678 patients referred with unexplained learning difficulties and/or autism, with or without other congenital abnormalities. Eight and five unrelated individuals, respectively, were identified with microdeletions and microduplications in 17p13.3. Results Comparisons with six previously reported microdeletion cases identified a 258 kb critical region, encompassing six genes including CRK (encoding Crk) and YWHAE (encoding 14-3-3ε). Clinical features included growth retardation, facial dysmorphism and developmental delay. Notably, one individual with only subtle facial features and an interstitial deletion involving CRK but not YWHAE suggested that a genomic region spanning 109 kb, encompassing two genes (TUSC5 and YWHAE), is responsible for the main facial dysmorphism phenotype. Only the microduplication phenotype included autism. The microduplication minimal region of overlap for the new and previously reported cases spans 72 kb encompassing a single gene, YWHAE. These genomic rearrangements were not associated with low-copy repeats and are probably due to diverse molecular mechanisms. Conclusions The authors further characterise the 17p13.3 microdeletion and microduplication phenotypic spectrum and describe a smaller critical genomic region allowing identification of candidate genes for the distinctive facial dysmorphism (microdeletions) and autism (microduplications) manifestations.

Definition of a critical region on chromosome 18 for congenital aural atresia by arrayCGH

Deletions of the long arm of chromosome 18 occur in approximately 1 in 10,000 live births. Congenital aural atresia (CAA), or narrow external auditory canals, occurs in approximately 66% of all patients who have a terminal deletion 18q. The present report describes a series of 20 patients with CAA, of whom 18 had microscopically visible 18q deletions. The extent and nature of the chromosome-18 deletions were studied in detail by array-based comparative genomic hybridization (arrayCGH). High-resolution chromosome-18 profiles were obtained for all patients, and a critical region of 5 Mb that was deleted in all patients with CAA could be defined on 18q22.3-18q23. Therefore, this region can be considered as a candidate region for aural atresia. The array-based high-resolution copy-number screening enabled a refined cytogenetic diagnosis in 12 patients. Our approach appeared to be applicable to the detection of genetic mosaicisms and, in particular, to a detailed delineation of ring chromosomes. This study clearly demonstrates the power of the arrayCGH technology in high-resolution molecular karyotyping. Deletion and amplification mapping can now be performed at the submicroscopic level and will allow high-throughput definition of genomic regions harboring disease genes.

Genotype-phenotype mapping of chromosome 18q deletions by high-resolution array CGH: An update of the phenotypic map

Partial deletions of the long arm of chromosome 18 lead to variable phenotypes. Common clinical features include a characteristic face, short stature, congenital aural atresia (CAA), abnormalities of the feet, and mental retardation (MR). The presence or absence of these clinical features may depend on the size and position of the deleted region. Conversely, it is also known that patients whose breakpoints are localized within the same chromosome band may exhibit distinct phenotypes. New molecular techniques such as array CGH allow for a more precise determination of breakpoints in cytogenetic syndromes, thus leading to better‐defined genotype–phenotype correlations. In order to update the phenotypic map for chromosome 18q deletions, we applied a tiling resolution chromosome 18 array to determine the exact breakpoints in 29 patients with such deletions. Subsequently, we linked the genotype to the patients phenotype and integrated our results with those previously published. Using this approach, we were able to refine the critical regions for microcephaly (18q21.33), short stature (18q12.1‐q12.3, 18q21.1‐q21.33, and 18q22.3‐q23), white matter disorders and delayed myelination (18q22.3‐q23), growth hormone insufficiency (18q22.3‐q23), and CAA (18q22.3). Additionally, the overall level of MR appeared to be mild in patients with deletions distal to 18q21.33 and severe in patients with deletions proximal to 18q21.31. The critical region for the ‘typical’ 18q‐phenotype is a region of 4.3 Mb located within 18q22.3‐q23. Molecular characterization of more patients will ultimately lead to a further delineation of the critical regions and thus to the identification of candidate genes for these specific traits.

Clinical and cytogenetic characterization of 13 Dutch patients with deletion 9p syndrome: Delineation of the critical region for a consensus phenotype†

The deletion 9p syndrome is caused by a constitutional monosomy of part of the short arm of chromosome 9. It is clinically characterized by dysmorphic facial features (trigonocephaly, midface hypoplasia, and long philtrum), hypotonia and mental retardation. Deletion 9p is known to be heterogeneous and exhibits variable deletion sizes. The critical region for a consensus phenotype has been reported to be located within a ∼4–6 Mb interval on 9p22. In the present study, deletion breakpoints were determined in 13 Dutch patients by applying fluorescence in situ hybridization (FISH) and in some specific cases by array‐based comparative genomic hybridization (array CGH). No clear genotype–phenotype correlation could be established for various developmental features. However, we were able to narrow down the critical region for deletion 9p syndrome to ∼300 kb. A functional candidate gene for trigonocephaly, the CER1 gene, appeared to be located just outside this region. Sequence analysis of this gene in nine additional patients with isolated trigonocephaly did not reveal any pathogenic mutations.