Printer Friendly

Inflammatory lung disease in Rett syndrome.

1. Introduction

Rett syndrome (RTT), for a long time included among the Autism Spectrum Disorders (ASDs), is a nosologically distinct, genetically determined neurological entity associated in up to 95% of cases to de novo loss-of-function mutations in the X-chromosome-linked gene encoding the methyl-CpG-binding protein 2 (MeCP2) [1]. MeCP2, a ubiquitous protein particularly abundant in brain, is known to either activate or repress transcription [2, 3], is critical to the function of several types of cells (i.e., neurons and astroglial cells), and targets several genes essential for neuronal survival, dendritic growth, synaptogenesis, and activity dependent plasticity [4].

In its classical clinical presentation, RTT affects heterozygous females and shows a typical 4-stage neurological regression after 6 to 18 months of apparently normal development. RTT is a relatively rare disease, affecting about 1: 10,000 female live births, although it represents the second most common cause of severe intellectual disability in the female gender [5, 6]. Preserved speech, early seizure, and congenital are well-known atypical variants often linked to mutations in genes other than MECP2, that is, the cyclin-dependent kinase-like 5 (CDKL5) in the early seizure variant and the forkhead boxG1 (FOXG1) in the congenital variant 6, 7].

Breathing disorders are considered a hallmark feature of RTT and represent a major clinical challenge [8]. To date, a large number of studies have been focusing on this particular characteristic of the disease, both in the clinical and experimental environments. Breathing abnormalities in RTT variably include/feature breath holdings, apneas, apneusis, hyperventilation, rapid shallow breathing, and spontaneous Valsalva maneuvers [9]. In particular, a highly irregular respiratory rhythm particularly during daytime is considered among the key symptoms of RTT [9-11]. Cumulating evidence indicates a predominantly hyperventilatory pattern with increased respiratory frequency and decreased expiratory duration, which is associated with frequent episodes of breath-holding/obstructive apnea or Valsalva breathing against closed airways during wakefulness [12-14]. However, the breath-holding/obstructive apnea phenotype of RTT is often confused in the related clinical literature with central apnea, which has fundamentally distinct neurological mechanisms [9, 15-26]. The wide spectrum of respiratory disorders detectable in RTT patients has been historically credited to brainstem immaturity and/or cardiorespiratory autonomic dysautonomia [9, 27, 28]. However, as the pathogenesis of the respiratory dysfunction in RTT appears far from being completely understood, alternative or complementary hypotheses can be formulated [29].

In particular, the potential role of oxidative stress (OS) mediators and the role of the lung itself in the pathogenesis of the respiratory dysfunction in the human disease are incompletely understood. More recently, biochemical evidence of redox imbalance and, in particular, enhanced lipid peroxidation, in blood samples from RTT patients, was further confirmed in primary skin fibroblasts cultures from patients [30-37], although the nature of the relationship, that is, whether causal or correlational, between MECP2 gene mutation and abnormal redox homeostasis remains currently unclear. Significantly increased pulmonary alveolar-arterial gradient for O2, highly suggestive for an abnormal pulmonary gas exchange, has been previously described by our group in the majority of the examined RTT patients [29] and was found to be related in about a half of the cases to a pulmonary radiological picture partially mimicking that of the respiratory bronchiolitis-associated interstitial lung disease (RB-ILD) [38], one of the three lung conditions showing the stronger epidemiological association with tobacco smoke [39, 40].

However, to date, no information exists regarding the lung pathological lesions underlying the radiological changes observed in RTT patients.

Aims of the present study were to characterize the possible role of pulmonary gas exchange abnormality (GEA) in the pathogenesis of redox imbalance and respiratory dysfunction in RTT and to evaluate lung histology in an experimental mouse model of MeCP2 deficiency.

2. Methods

2.1. Subjects. In the present study, a total of n = 228 female patients with a clinical diagnosis of typical RTT and demonstrated MECP2 gene mutation were recruited (mean age, 12.9 [+ or -] 7.9 years; range, 1.5-32 years). RTT diagnosis and inclusion/exclusion criteria were based on a revised nomenclature consensus [6]. Clinical severity was assessed by the use of the clinical severity score (CSS), a specifically validated clinical rating system based on 13 individual ordinal categories measuring clinical features common in the disease [7]. Respiratory dysfunction on a clinical basis was categorized based on the corresponding Percy scale item (+ as minimal hyperventilation and/or apnea; ++ as intermittent hyperventilation and/or apnea; and +++ as hyperventilation and/or apnea with cyanosis) [41].

The corresponding z-scores for body weight, height, head circumference, and body mass index were calculated on the basis of validated RTT-specific growth charts [42]. Clinical stages distribution was: stage I (n = 4), stage II (n = 69), stage III (n = 92), and stage IV (n = 63). All the patients were admitted to the Rett Syndrome National Reference Centre of the University Hospital of the Azienda Ospedaliera Universitaria Senese. A total of 114 healthy and typically developed female subjects of comparable age (mean age, 12.9 [+ or -] 7.8 years; range, 1.6-32 years) were also enrolled in the study as a control population. Blood samplings from the control group were performed during routine health checks, sports, or blood donations obtained during the periodic checks. All the examined subjects were on a typical Mediterranean diet. The study was conducted with the approval by the Institutional Review Board and all informed consents were obtained from either the parents or the legal tutors of the enrolled patients.

2.2. Oxidative Stress (OS) Markers and Antioxidant Defence Evaluations

2.2.1. Blood Sampling. Blood was collected in heparinized tubes and all manipulations were carried out within 2 h after sample collection. An aliquot (90 [micro]L) of each sample was used for reduced and oxidized glutathione assay. Blood samples were centrifuged at 2400 x g for 15 min at 4[degrees]C; the platelet poor plasma was saved and the buffy coat was removed by aspiration. RBCs were washed twice with physiologic solution (150 mM NaCl). An aliquot of packed erythrocytes was resuspended in Ringer solution (125 mM NaCl, 5 mM KCl, 1 mM MgS[O.sub.4], 32 mM N-2 hydroxyethylpiperazine-N-2-ethanesulfonic acid (HEPES), 5mM glucose, and 1mM Ca[Cl.sub.2]), pH 7.4 as a 50% (vol/vol) suspension for the determination of intraerythrocyte NPBI. Plasma was used for the NPBI assay.

2.2.2. Intraerythrocyte and Plasma Non-Protein-Bound Iron (IE-NPBI). Generally, NPBI is considered not only an OS marker but a prooxidant factor. In particular, IE-NPBI is a critical marker of hypoxia. IE-NPBI (nmol/mL erythrocyte suspension) was determined as a desferrioxamine- (DFO) iron complex (ferrioxamine) as previously reported [29] Plasma NPBI (nmol/ml) was determined as above reported for IE-NPBI [29].

2.2.3. Plasma [F.sub.2]-Isoprostanes ([F.sub.2]-IsoPs). [F.sub.2]-IsoPs, generated by free radical-catalyzed peroxidation of phospholipid-bound arachidonic acid, are considered specific and reliable OS markers in vivo. [F.sub.2]-IsoPs were determined by a gas chromatography/negative ion chemical ionization tandem mass spectrometry (GC/NICI-MS/MS) analysis after solid phase extraction and derivatization steps [43]. For [F.sub.2]-IsoPs the measured ions were the product ions at m/z 299 and m/z 303 derived from the [[M-181].sup.-] precursor ions (m/z 569 and m/z 573) produced from 15-[F.sub.2t]-IsoPs and PG[F.sub.2a]-d4, respectively [43].

2.2.4. Blood Reduced and Oxidized Glutathione. Glutathione ([gamma]-L-glutamyl-L-cysteinyl-glycine) is a tripeptide that plays an important role in protecting cells and tissues against OS [44]. Under nonoxidative and nitrosative stress conditions, over 98% of the glutathione is considered to be in the reduced form (GSH) [45], whereas under oxidative conditions GSH is converted to glutathione disulfide (GSSG), its oxidized form, with a resulting decrease in the GSH/GSSG ratio. As blood glutathione concentrations may reflect glutathione status in other less accessible tissues, measurement of both GSH and GSSG in blood has been considered essential as an index of whole-body glutathione status and a useful indicator of antioxidant defence [46]. Specifically, the GSH/GSSG ratio reflects the cellular redox status. Blood GSH and GSSG levels were determined by an enzymatic recycling procedure according to Tietze [47] and Baker et al. [48].

2.3. Cardiorespiratory Monitoring. In order to analyze the occurrence of apnoeas and hypopneas, breathing monitoring was carried out in RTT patients during wakefulness and sleep state by using portable polygraphic screening devices (SOMNOwatchTM plus, SOMNOmedics, Randersacker, Germany; importer for Italy Linde Medicale srl) for a mean recording time of 13 [+ or -] 0.5 h for each state. Monitoring included nasal airflow, arterial oxygen saturation by pulse oximetry, and respiratory efforts by abdominal and thoracic bands. Breathing patterns were analyzed for the presence of apnoeas and hypopnoeas according to the standardized definitions by the American Academy of Sleep Medicine [49] and the American Academy of Pediatrics [50]. Apnoeas were defined as a > 90% airflow decrease for 10 sec, while hypopnoeas were defined as a > 50% airflow reduction for ~10 sec associated with a decrease of 3% in oxygen saturation [49]. Apnoeas were categorized as obstructive (i.e., cessation of airflow for 10 sec with persistent respiratory effort), central (i.e., cessation of airflow for 10 sec with no respiratory effort), and mixed (an apnea that begins as a central apnea and ends up as an obstructive apnea). Apnoeas were further categorized as mild (10 to 15 sec), moderate (15 to 30 sec), and severe (> 30 sec) on the basis of their recorded duration. The apnea-hypopnea index (AHI) was defined as the number of obstructive and central apnoeas and hypopnoeas per hour of sleep and calculated by dividing the total number of events by the total sleep time. An AHI > 15 during sleep was considered to be indicative of obstructive sleep apnea/hypopnea syndrome (OSAHS). All records were reviewed by a pneumologist with a longstanding expertise in OSAHS (i.e., coauthor M.R.).

2.4. Pulmonary Gas Exchange Analysis. Pulmonary gas exchange was evaluated from direct measurements of total volume ([V.sub.tot]), respiratory rate, and expiratory fractions of C[O.sub.2] and [O.sub.2] by using a portable, commercially available gas analyzer (Hanky Hapy, version 1.2; Ambra Sistemi; Pianezza, Turin, Italy), as previously described [29]. The method to evaluate pulmonary gas exchange works essentially as a multicompartment model (Figure 1) and is essentially based on the classical West function [51]. Air gas sampling was obtained by applying a facial mask of appropriate size connected to the gas analyzer. Low invasivity and the easy-to-use features of the method allowed us to evaluate a relatively large population size of patients. Actually, the methodology does not require patient's cooperation and is therefore easily applicable to RTT patients and has been proven to be sufficiently simple, noninvasive, accurate, and precise in determining alveolar-arterial gradient lung exchange for [O.sub.2] and ventilation/perfusion ratio (V/Q) inequalities. Respiratory rate, total ventilation, and expired gas composition were measured during either a 60-sec or 120-sec time period. V/Q distribution parameters were calculated by a minimizing mathematical function in order to reset to zero the differences between measured and calculated Pa[O.sub.2] and PaC[O.sub.2]. All respiratory measurements were carried out in duplicate, and the averages used for data analysis. Arterial blood for gas analyses was sampled from either the humeral or the radial artery, and Pa[O.sub.2], PaC[O.sub.2], and pH values were determined using a commercially available blood gas analyzer (ABL520 Radiometer; Radiometer Medical A/S; Copenhagen, Denmark). Ventilation-perfusion (V/Q) inequalities (i.e., GEA) were classified as low, high, mixed, and simple mismatch. A "low" pattern indicates the presence of perfusion in poorly ventilated pulmonary areas; a "high" pattern points out the existence of high ventilation in poorly perfused pulmonary areas; a mixed pattern indicates a combination of the former two patterns; a simple "mismatch" was defined as a V/Q uncoupling showing a modest fraction of low V/Q ratios (1 to 0.1) and a modest fraction of high V/Q ratios (1to 10). In order to account for the low PaC[O.sub.2] values often encountered in RTT patients, standard Pa[O.sub.2] was calculated with the formula Pa[O.sub.2] = 1.66 x PaC[O.sub.2] + Pa[O.sub.2-] 66.4, according to Sorbini et al. [52].

2.5. RTT Mouse Model: Murine Lung Histology. A total of (n = 4) Mecp2 null mice and (n = 4) wild-type matched mice were examined. Experimental subjects were derived from heterozygous B6.129SF1-Mecp2tm1Jae knockout females (Mecp2+/-) 53]. Females were originally crossed to C57BL6/J for one generation, followed by breeding amongst offspring of the same generation with breeder changes, and were maintained on a mixed background. Mixed background reduced mortality and was necessary to obtain the high numbers of mice required by extensive analysis. Age-matched littermates were used in all experiments to control for possible effects of genetic background unrelated to the Mecp2 mutation [54]. Mice were killed by decapitation at the thirty-eighth day of life; their lungs were removed rapidly and immediately frozen on liquid nitrogen. National and institutional guidelines were used for the care and use of animals, and approval for the experiments was obtained. Lungs were inflated with neutral buffered 10% formalin solution for about 24 h until adequate fixation. Each lung was dissected and sections were embedded in paraffin. Several 5 micrometres sections from each inclusion were stained with a standard hematoxylin and eosin staining protocol.

2.6. Statistical Data Analysis. All variables were tested for normal distribution (D'Agostino-Pearson test). Data were presented as means [+ or -] standard deviation or medians and interquartile range for normally distributed and non-Gaussian continuous variables, respectively. Differences between RTT and control groups were evaluated using independent-sample t-test (continuous normally distributed data), Mann-Whitney rank sum test (continuous nonnormally distributed data), chi-square statistics (categorical variables with minimum number of cases per cell [greater than or equal to] 5) or Fisher's exact test (categorical variables with minimum number of cases per cell < 5), one-way analysis of variance (ANOVA), Student-Newman-Keuls post hoc test, or Kruskal-Wallis test, as appropriate. Associations between variables were tested by either parametric (Pearson's coefficients) or nonparametric univariate (Spearman's rho) regression analysis. Predictive accuracy of apneas frequency/severity in identifying enhanced OS markers in RTT patients was calculated using a receiver operating characteristic curve (ROC) analysis, and an area under the curve value > 0.5 was accepted to indicate good discrimination. The MedCalc version 12.1.4 statistical software package (MedCalc Software, Mariakerke, Belgium) was used for data analysis and a two-tailed P < 0.05 was considered to indicate statistical significance.

3. Results

3.1. Clinical Respiratory Dysfunction. Relevant demographic clinical characteristics for the examined RTT population are shown in Table 1. According to the specifically related items in the severity scoring system, all patients showed clinical signs for a respiratory dysfunction at different degrees, with moderate or severe dysfunction being detectable on a clinical basis in 81.6% (186/228) of the RTT patients.

3.2. Pulmonary Gas Exchanges. Gas pulmonary exchange investigations demonstrated the existence of a variety of ventilation-perfusion inequalities (Figure 2 and Table 2) in more than 3/4 (i.e., 80.7%) of the whole RTT population; a "low" pattern (i.e., presence of perfusion in poorly ventilated pulmonary areas) was observed in 64 patients (28.1% of the examined whole RTT population), a "high" pattern (i.e., high ventilation in poorly perfused pulmonary areas) in 73 cases (32%), and a simple "mismatch" in 11 cases (4.8%), while a "mixed" pattern was present in 36 patients (15.8%). Overall, only 19.3% (44/228) of the RTT population showed a physiological (i.e., coupled M/Q) gas exchange pattern (RTT versus controls, chi-square: 138.472, DF = 4, P < 0.0001; chi-square for trend: 56.154, DF = 1, P < 0.0001).

Pulmonary gas exchanges parameters (Table 2) detected a general trend toward hyperventilation in the RTT patients, with mean total ventilation rates ([V.sub.tot]) of 6.3 [+ or -] 3.6 L/min (95% C.I. for the mean 5.8 to 6.7) versus 5.2 [+ or -] 2.1 L/min in the control subjects (P = 0.0028). Hyperventilation was absent in the "low" pattern of GEA, while being extreme in the "high" GEA pattern. Likewise, alveolar ventilation was the largest in the "high" pattern subpopulation of patients, with alveolar ventilation values usually > 3 L/min for all GEA subcategories that is volumetrically consistent ventilation.

Blood gas analyses in RTT patients confirmed the presence of a relative hypoxia (Pa[O.sub.2]: 87.5 [+ or -] 18.1 versus 98.7 [+ or -] 6.5 mmHg, difference [+ or -] SE: 11.2 [+ or -] 1.75, 95% C.I.: 7.76 to 14.6, P < 0.0001) and hypocapnia (PaC[O.sub.2]:35.2 [+ or -] 7.5 versus 42.8 [+ or -] 5.8 mmHg, difference [+ or -] SE: 7.6 [+ or -] 0.08, 95% C.I.: 6.02-9.18, P < 0.0001), whereas blood pH was comparable between RTT and healthy controls (7.417 [+ or -] 0.043 versus 7.413[+ or -] 0.045; difference [+ or -] SE: -0.004 [+ or -] 0.005854, 95% C.I.: -0.0138 to 0.005854, P = 0.4252). When hypocapnia was accounted for, standard Pa[O.sub.2] values in patients were found to be on average 17.6 [+ or -] 1.5% lower than those of their healthy control counterparts (Pa[O.sub.2]: 81.1 [+ or -] 15.3 versus 98.7 [+ or -] 6.5, difference [+ or -] SE: 17.6 [+ or -] 1.5, 95% C.I.: 14.66 to 20.54, P < 0.0001) despite a normal-to-increased total volume ([V.sub.tot]) 5.8 [+ or -] 2.97 versus 5.2 [+ or -] 2.3 L/min, difference [+ or -] SE: -0.6 [+ or -] 0.317,95% C.I.: -1.224 to 0.0239, P = 0.0594.

Remarkably, larger differences in hyperventilation were associated with consistently smaller intergroup differences (1-way ANOVA, P = 0.025) in Pa[O.sub.2] and even smaller differences when hypocapnia was accounted for (standard Pa[O.sub.2], P = 0.082), thus indicating a reduced efficiency of pulmonary exchange despite normal pH values. However, the physiological dead space, as calculated by the Bohr equation, was found to be at the upper physiological limits (i.e., 30 to 45% of the [V.sub.t] in the healthy control population) in the "no mismatch," "simple mismatch," and "low" patterns (41.2 to 45.0 [V.sub.t]%), whereas it appears to be increased up to 55.4 [+ or -] 10.8 [V.sub.t]% and 54.7 [+ or -] 15.5 [V.sub.t]% in "high" and "mixed" patterns, respectively. These findings confirm the occurrence of a reduced efficiency of pulmonary exchanges in the RTT population, with a statistically significant relationship between respiratory rate and Bohr's physiological dead space (rho = 0.144, P = 0.0303). Overall, oxygen uptake ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) and carbon dioxide production ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) values appear to be lower than those of healthy controls subjects ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]: 250 to 300mL/min and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] to 250mL/min, resp.). Likewise, respiratory exchange ratios ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]) in the RTT patients were accordingly higher than those observed in healthy controls (1.56 [+ or -] 1.23 versus 0.81 [+ or -] 0.32, P < 0.0001).

3.3. Redox and Antioxidant Status. The results of the redox and antioxidant markers in RTT patients showed significantly increased plasma levels of non-protein-bound iron (NPBI) (~2-fold), [F.sub.2]-isoprostanes ([F.sub.2]-IsoPs) (~2.9-fold), reduced glutathione (GSH) (~1.4-fold), oxidized glutathione (GSSG) (~50-fold), and intraerythrocyte NPBI (IE-NPBI) (~1.5-fold) as compared to healthy control subjects (Table 3). Consequently, a significantly decreased GSH/GSSG ratio (~ -15-fold) in patients was evidenced.

3.4. Cardiorespiratory Monitoring. Cardiorespiratory monitoring showed a significant prevalence of obstructive apnoeas both during the sleep and the wakefulness states in RTT patients, with median rates of obstructive apnoeas of 177/h and 6.2/h, respectively (Table 4). Of note, obstructive episodes were more prevalent as compared to central events by 25.3- and 15.5-fold during the wakefulness and sleep state, respectively. The lowest recorded SpO2 values during the apnoeic events were 78.8 [+ or -] 13.1%. Apneas during the sleep phase were detectable in 63.6% (145/228) of patients, with a mean AHI of 15.9 [+ or -] 4.69. Positive criteria for OsAHS (AHI > 15) were present in 27.2% (62/228) of the whole RTT patients population.

3.5. Relationship between Redox Imbalance and Apnea Frequency/Severity. Statistically significant positive correlations were observed between recording of apneas, independently of the degree of severity, and IE-NPBI (rho coefficients, range: 0.324 to 0.358; P values, range: 0.0024 to 0.0089) or GSSG (rho coefficients range: 0.258 to 0.267; P values, range: 0.0392 to 0.0156) (Table 5). On the other hand, positive relationships between apneas and p-NPBI (rho: 0.265, P = 0.0346) or [F.sub.2]-IsoPs (rho: 0.305, P = 0.0142) were also observed but limited to the most severe events only. A significant inverse relationship between moderate apneas and GSH to GSSG ratio was present (rho: -0.247, P = 0.0488). An average number of > 7.4/h for total apneas, > 0.8/h for moderate apneas, and > 2 for severe apneas (either recorded during wakefulness or sleep states) were found to be predictive for increased IE-NPBI plasma levels in patients, with 50% to 80.7% sensitivity, 55.3% to 82.02% specificity, 55.3% to 65% positive predictive value, and 62.7% to 80.8% negative predictive value (P values for the AUC, range: 0.0044 to 0.0163) (Table 6). In contrast, frequency/severity of the recorded apneas was not predictive for plasma levels of F2-IsoPs in RTT patients (P values for the AUCs, range: 0.3149 to 0.9487).

3.6. Relationship between Redox Imbalance and Pulmonary Gas Exchange Abnormality. When evaluated as a function of GEA in RTT, striking differences in the redox/antioxidant markers levels were detectable among the different categories of pulmonary V/Q inequality, ranging from values comparable to those of the control group in the "no mismatch" group to significant redox/antioxidant imbalance in the various GEA patterns (Table 7). In particular, [F.sub.2]-IsoPs plasma levels were approximately proportional to the degree of severity for V/Q abnormality, with increase of ~1.7-fold for "simple mismatch," ~2.4-fold for "low" patterns, ~2.8-fold for "high" patterns, and ~3.7-fold for "mixed" patterns, as compared to patients without detectable GEA.

3.7. Mutant Mecp2 Murine Lung Histology. The results of lung histology in the Mecp2 null RTT mouse models showed a picture of nonspecific lymphocytic bronchiolitis associated with lymphocytic vasculitis (Figures 3(b) and 3(d)) and desquamative alveolitis in a half of the examined mutant mice, whereas no significant histological abnormalities were observed in the wt animals.

4. Discussion

Respiratory dysfunction in RTT appears to be far more complex than previously thought. Specifically, the findings of the present study indicate that, besides brainstem dysfunction, several intertwined critical factors, either directly or indirectly related to the disease, appear to concur to adversely affect respiratory function in RTT patients. In particular, our findings strongly support the hypothesis that the respiratory behavior in RTT, historically credited to neurological dysfunction, can be considered as the result of a previously unrecognized inflammatory process and/or abnormal immune response [55].

Abnormal pulmonary gas exchange, as the result of the imbalance between V and Q, is a main cause of hypoxemia [56]. While confirming the coexistence of a relative hypoxia (-17.6 [+ or -] 1.5% as compared to a cohort of healthy controls; 95% C.I.: -14.66 to -20.54) and abnormal pulmonary gas exchange in over 3/4 of the patients with typical RTT, we identified for the first time the relative distribution for the different GEA patterns. GEA was detectable in ~80% of the RTT patients, with "high" and "low" patterns dominating over "mixed" and "simple mismatch" types of pulmonary V/Q inequalities. This V/Q behavior here observed could be linked to either the presence of unventilated pulmonary areas in the low patterns (i.e., unrecognized pulmonary dysventilation or microatelectasis) or unperfused areas in the high patterns (i.e., unrecognized pulmonary microembolism). Therefore, our findings strongly suggest that GEA is a key feature of respiratory dysfunction in RTT that brainstem immaturity [9, 12] and/or cardiorespiratory autonomic dysautonomia [13, 14], both repeatedly evoked in the disease, are per se evidently unable to explain. It is interesting to observe that several features of the respiratory behaviour observed in RTT patients, chiefly hyperventilation, can be interpreted as compensatory mechanisms rather than the effects of dysfunctional brainstem activity and/or cardiorespiratory autonomic dysautonomia. However, hyperventilation, as a likely compensatory mechanism in order to overcome the negative effects of GEA, can lead to an adverse increase of the physiological dead space, as thus reducing the fraction of ventilation that effectively participates in alveolar gas exchanges. In addition, hyperventilation can lead to severe hypocapnia, a very frequent finding in RTT, thus further decreasing the effective (i.e., normalized to PaC[O.sub.2]) Pa[O.sub.2] levels. The observed decrease in [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] could also be potentially regarded as possible effects of compensatory mechanisms, although more investigation is needed to understand this unexpected feature of the RTT lung pathophysiology.

OSAHS is described as repetitive obstructions of the upper airways during sleep, causing concomitant episodes of systemic hypoxia and associated cardiovascular and metabolic pathologies, with an estimated prevalence of 0.7% to 1.8% in the general paediatric population [57-62] and of 2% for adult women and 4% for adult men [63-65]. The condition can be difficult to diagnose clinically, although even mild-to-moderate obstructive sleep apnoea can result in adverse neurobehavioral consequences and negatively affect quality of life [58, 59]. Our cardiorespiratory monitoring data in a relatively large cohort of RTT patients indicate a significantly increased prevalence of OSAHS in this patient population (i.e., 27.2%) and confirm the presence of clinically significant apneas also during wakefulness. These findings indicate that RTT should be added to the list of the already known heterogeneous pediatric conditions carrying an increased risk for OSAHS, including Down's syndrome, neuromuscular disease, craniofacial abnormalities, achondroplasia, mucopolysaccharidoses, and Prader-Willi syndrome. Although the overwhelming majority of previous polysomnographic studies in RTT patients have reported a prevalence of apnoeas of central origin [15-26], our findings indicate that obstructive apnoeas are far more common in typical RTT than previously reported.

Human and experimental evidence indicate that OSAHS and intermittent hypoxia can be associated with enhanced OS, although conflicting reports exist [66-76]. In the present study, we confirm the coexistence of a significant redox abnormality in RTT patients. However, the relationship between upper airways obstruction/intermittent hypoxia and OS status in RTT appears to be limited to the generation of a prooxidant status, as indicated by the link here observed between IE-NPBI, but not F2-IsoPs, and apneas. Iron is a major player in redox reactions, as it has been known for a long time that redox-active iron is one of the most active sources of OS [77]. Furthermore, iron release is much higher under hypoxic conditions than under normoxia [78, 79], so that hypoxia paradoxically represents a condition of OS that is consistent with a condition of ischemia-reperfusion injury. Released iron can diffuse out of the erythrocytes and the diffusion is higher with hypoxic erythrocytes [79]. Intermittent hypoxia can affect the stability of the bond of iron to the tetrapyrrole ring of protoporphyrin, thus releasing iron inside the erythrocytes, along with hemoglobin autoxidation [80]. However, it becomes clear that mechanisms other than intermittent hypoxia should be the major sources of enhanced OS in RTT. Actually, in the present study, we observed a very intimate relationship between redox abnormality and GEA in RTT. These results suggest that chronic, rather than intermittent, hypoxia resulting from a pulmonary V/Q inequality is likely the main source of systemic OS in RTT. To this regard, it is of relevance that we detected a dramatic increase of GSSG in RTT patients, with a parallel dramatic decrease of the GSH/GSSG ratio, thus strongly suggesting the coexistence of a chronic OS status.

While future studies are obviously needed to address this major topic, several other factors apparently concur to adversely affect the respiratory function and, either directly or indirectly, contribute to pulmonary gas exchanges impairment in RTT patients. These factors likely include abnormal erythrocyte shape with oxidative membrane damage [81], microvascular dysplasia [82], alterations in the vascular/endothelial system [83], mitochondrial dysfunction [84-87], subclinical biventricular myocardial dysfunction [88], systemic oxidative stress [29, 35], and, chiefly, subclinical inflammatory processes [38, 55].

Over the last decade, several experimental animal models have been developed in which the Mecp2 allele has been modified to prevent production of a fully functional Mecp2 protein. In particular, several experimental mouse models of Mecp2 deficiency have been established in mice, ranging from null-Mecp2 mutations to specific point mutations mimicking those observed in humans, phenocopying several motor and cognitive features of RTT patients. In particular, the irregular breathing pattern observed in human RTT has been replicated in several mutant mouse models to varying degrees of fidelity, although the corresponding respiratory phenotype varies among different mouse strains [89-91], with mutations in the Mecp2 gene leading to disparate respiratory phenotypes. For instance, in Mecp2tm1.1Jae null (hemizygous) mice on a mixed-strain background [92], the principal phenotype is tachypnea along with hyperventilation similar to human RTT [93], whereas in Mecp2tm1.1Bird null or heterozygous mice on a pure C57BL/6J background [94] the principal phenotype is repetitive spontaneous central apnea [95-97], whereas the Mecp2tm1.1Bird male mice provide an excellent animal model of spontaneous central apnea and possibly obstructive apnea [28]. A more recent study indicates that a clinically relevant RTT endophenotype, that is, tachypnea with a shortened expiratory time, appears to be more faithfully reproduced in Mecp2tm1.1Jae female mice [98]. Despite the fact that MeCp2 mutant mouse models cannot model all aspects of the human RTT, certainly they do recapitulate many aspects of the disease and are generally accepted as excellent tools to study MeCP2 function. In this context, our findings of a lymphocytic bronchiolitis in a half of the examined Mecp2-null mice are highly suggestive of a previously unrecognized inflammatory lung disease in RTT patients and are well fitting with our prior observations of radiological features at high-resolution computed tomography partially overlapping with those of RB-ILD [38] and including micronodules (i.e., inflammatory infiltrates in the smaller airways, such as terminal bronchioles and/or alveoli), "ground glass opacities" (i.e., radiological signs of alveolar inflammation), and, remarkably, thickening of bronchiolar walls (a radiological sign of inflammatory infiltrates in the terminal bronchioles). Further studies are obviously needed in different Mecp2-mutant animal models in order to ascertain prevalence and possible differences related to different mouse strains.

Cumulating evidence indicates that RTT is a multisystemic disease, which, besides the brain, is known to affect several organs and systems, including the autonomic nervous system [12-14], microvascular/endothelial system [12, 13], bone [99], heart [88, 100], red blood cells [81], the gastrointestinal tract [101], and the immune system [102, 103]. Our study strongly supports the concept that the lung is a previously unrecognized major target organ in this genetically determined neurodevelopmental disease and that pulmonary GEA is likely a key feature within the multisystemic characteristics of the disease.

5. Conclusions

The findings of the present clinical study confirm the emerging concept according to which no single putative mechanism can account for all the complexity of the respiratory behaviour exhibited by RTT patients. The present study indicates that (1) pulmonary GEA, not brainstem immaturity, is likely the key feature of respiratory dysfunction in RTT; (2) the RTT-related GEA is likely the result of several contributing factors, involving OS and chronic subclinical inflammation; and (3) terminal bronchioles and alveoli are likely a major, under-recognized, inflammatory target of the disease. Moreover, these data confirm the clinical relevance of respiratory dysfunction in this rare neurological disease as a valuable pathophysiological model for a better understanding of the complex involvement of the lung in a multisystemic disease. Our findings strongly support the hypothesis that the respiratory behavior in RTT, historically credited to neurological dysfunction, is rather the result of an inflammatory process and/or abnormal immune response.

Abbreviations

(A-a)[O.sub.2]:            [O.sub.2] alveolar-arterial pulmonary
                           gradient Apnea-hypopnea index
ASDs:                      Autism Spectrum Disorders
AUC:                       Area under the curve
Bohr's DS:                 Physiological dead space as calculated by
                             the Bohr equation
CDKL5:                     Cyclin-dependent kinase-like 5
[F.sub.2]-IsoPs:           [F.sub.2]-isoprostanes
FOXG1:                     Forkhead box G1
GEA:                       Lung gas exchange abnormality
GSH:                       Reduced glutathione
GSSG:                      Oxidized glutathione
IE-NPBI:                   Intraerythrocyte non-protein-bound iron
MeCP2:                     Methyl-CpG-binding protein 2
OS:                        Oxidative stress
OSAHS:                     Obstructive sleep apnea-hypopnea
                             syndrome
PaC[O.sub.2]:              Partial arterial pressure of C[O.sub.2]
Pa[O.sub.2]:               Partial arterial pressure of [O.sub.2]
P-NPBI:                    Plasma non-protein-bound iron
RB-ILD:                    Respiratory bronchiolitis-associated
                             interstitial lung disease
[Q.sub.s]/[Q.sub.t]:       Pulmonary functional shunting (i.e.,
                           venous admixture)
RTT:                       Rett syndrome
M/Q:                       Lung ventilation-to-perfusion ratio
[V.sub.t]:                 Tidal volume
[V.sub.tot]:               Total ventilation
[MATHEMATICAL EXPRESSION
NOT REPRODUCIBLE IN
ASCII]:                    C[O.sub.2] production
[MATHEMATICAL EXPRESSION
NOT REPRODUCIBLE IN
ASCII]:                    [O.sub.2] uptake.


http://dx.doi.org/10.1155/2014/560120

Conflict of Interests

The authors declare that they have no conflict of interests.

Authors' Contribution

Claudio De Felice and Marcello Rossi contributed equally to this work, and Tommaso Pizzorusso and Joussef Hayek equally supervised this work.

Acknowledgments

The present research project has been funded by the Tuscany Region [Bando Salute 2009, "Antioxidants (w-3 Polyunsaturated Fatty Acids, lipoic acid) supplementation in Rett syndrome: A novel approach to therapy"], Italy. The authors are grateful to the Associazione Italiana Rett, (A.I.R., President: Mrs. Lucia Dovigo), the Round Table 41 Club of Siena, the Kiwanis Club of Siena, and Drs. Pierluigi Tosi, Silvia Briani, and Roberta Croci from the Administrative Direction of the Azienda Ospedaliera Senese (AOUS) for continued encouragement and support. They acknowledge the Medical Genetic Unit of the Siena University (Head: Professor Alessandra Renieri) for gene mutations analysis. They thank Roberto Faleri (Medical Central Library of the University of Siena) for online bibliographic research assistance. The authors are indebted to actors Andrea, Angela, and Benedetta Giuntini, actor-painter Gloria Grazzini, singers Maria Grazia di Valentino, Ilaria Savini, and Veronica Costa, and musicians Luca Savazzi, Alessandro De Fazi, Alessandro Cei, and Simone Faraoni for generously donating their time and excellent artistic skills in order to test the physiological effects of voice and sounds on respiratory dysfunction in Rett girls. They are grateful to world-renown illustrator Maestro Roberto Innocenti, a recipient of the IBBY Christian Andersen Award as best illustrator, for his ongoing artistic efforts in order to popularize our research on Rett syndrome. The authors' special thanks go to the professional singer Matteo Setti (http://www.matteosetti.com) for having with his unique voice started exploring the bridge between science and art in Rett syndrome since 2007 by performing a large number of tests with the Rett girls in an experimental context, as well as participating in a number of congresses and charity events. They dedicate this work to all the Rett girls and their families.

References

[1] R. E. Amir, I. B. van den Veyver, M. Wan, C. Q. Tran, U. Francke, and H. Y. Zoghbi, "Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl- CpG-binding protein 2," Nature Genetics, vol. 23, no. 2, pp. 185-188, 1999.

[2] M. Chahrour, Y. J. Sung, C. Shaw et al., "MeCP2, a key contributor to neurological disease, activates and represses transcription," Science, vol. 320, no. 5880, pp. 1224-1229, 2008.

[3] Y. Li, H. Wang, J. Muffat et al., "Global transcriptional and translational repression in human-embryonic-stem-cell-derived Rett syndrome neurons," Cell Stem Cell, vol. 13, no. 4, pp. 446-458, 2013.

[4] J. Guy, H. Cheval, J. Selfridge, and A. Bird, "The role of MeCP2 in the brain," Annual Review of Cell and Developmental Biology, vol. 27, pp. 631-652, 2011.

[5] M. Chahrour and H. Y. Zoghbi, "The story of Rett syndrome: from clinic to neurobiology," Neuron, vol. 56, no. 3, pp. 422-437, 2007

[6] J. L. Neul, W. E. Kaufmann, D. G. Glaze et al., "Rett syndrome: revised diagnostic criteria and nomenclature," Annals of Neurology, vol. 68, no. 6, pp. 944-950, 2010.

[7] J. L. Neul, P. Fang, J. Barrish et al., "Specific mutations in Methyl-CpG-Binding Protein 2 confer different severity in Rett syndrome," Neurology, vol. 70, no. 16, pp. 1313-1321, 2008.

[8] J. M. Ramirez, C. S. Ward, and J. L. Neul, "Breathing challenges in Rett syndrome: lessons learned from humans and animal models," Respiratory Physiology & Neurobiology, vol. 189, no. 2, pp. 280-287, 2013.

[9] P. O. Julu, I. W. Engerstrom, S. Hansen et al., "Cardiorespiratory challenges in Rett's syndrome," The Lancet, vol. 371, no. 9629, pp. 1981-1983, 2008.

[10] A. M. Kerr, Y. Nomura, D. Armstrong et al., "Guidelines for reporting clinical features in cases with MECP2 mutations," Brain and Development, vol. 23, no. 4, pp. 208-211, 2001.

[11] B. Hagberg, F. Hanefeld, A. Percy, and O. Skjeldal, "An update on clinically applicable diagnostic criteria in Rett syndrome: comments to Rett syndrome clinical criteria consensus panel satellite to European Paediatric Neurology Society Meeting Baden Baden, Germany, 11 September 2001," European Journal of Paediatric Neurology, vol. 6, no. 5, pp. 293-297, 2002.

[12] P. O. Julu, A. M. Kerr, F. Apartopoulos et al., "Characterisation of breathing and associated central autonomic dysfunction in the Rett disorder," Archives of Disease in Childhood, vol. 85, no. 1, pp. 29-37, 2001.

[13] D. E. Weese-Mayer, S. P. Lieske, C. M. Boothby et al., "Autonomic nervous system dysregulation: breathing and heart rate perturbation during wakefulness in young girls with Rett syndrome," Pediatric Research, vol. 60, no. 4, pp. 443-449, 2006.

[14] D. E. Weese-Mayer, S. P. Lieske, C. M. Boothby, A. S. Kenny, H. L. Bennett, and J.-M. Ramirez, "Autonomic dysregulation in young girls with Rett syndrome during nighttime in-home recordings," Pediatric Pulmonology, vol. 43, no. 11, pp. 1045-1060, 2008.

[15] E. Lugaresi, F. Cirignotta, and P. Montagna, "Abnormal breathing in the Rett syndrome," Brain and Development, vol. 7, no. 3, pp. 329-333, 1985.

[16] F. Cirignotta, E. Lugaresi, and P. Montagna, "Breathing impairment in Rett syndrome," American Journal of Medical Genetics, vol. 24, no. 1, pp. 167-173, 1986.

[17] D. P. Southall, A. M. Kerr, and E. Tirosh, "Hyperventilation in the awake state: potentially treatable component of Rett syndrome," Archives of Disease in Childhood, vol. 63, no. 9, pp. 1039-1048, 1988.

[18] A. Kerr, D. Southall, P. Amos et al., "Correlation of electroencephalogram, respiration and movement in the Rett syndrome," Brain and Development, vol. 12, no. 1, pp. 61-68, 1990.

[19] C. L. Marcus, J. L. Carroll, S. A. McColley et al., "Polysomnographic characteristics of patients with Rett syndrome," Journal of Pediatrics, vol. 125, no. 2, pp. 218-224, 1994.

[20] B. Schluter, G. Aguigah, D. Buschatz, E. Trowitzsch, and F. Aksu, "Polysomnographic recordings of respiratory disturbances in Rett syndrome," Journal of Sleep Research, vol. 4, no. 1, pp. 203-207, 1995.

[21] M. Rohdin, E. Fernell, M. Eriksson, M. Albage, H. Lagercrantz, and M. Katz-Salamon, "Disturbances in cardiorespiratory function during day and night in Rett syndrome," Pediatric Neurology, vol. 37, no. 5, pp. 338-344, 2007

[22] G. M. Stettner, P. Huppke, J. Gartner, D. W. Richter, and M. Dutschmann, "Disturbances of breathing in Rett syndrome: results from patients and animal models," Advances in Experimental Medicine and Biology, vol. 605, pp. 503-507, 2008.

[23] G. d'Orsi, V. Demaio, F. Scarpelli, T. Calvario, and M. G. Minervini, "Central sleep apnoea in Rett syndrome," Neurological Sciences, vol. 30, no. 5, pp. 389-391, 2009.

[24] G. d'Orsi, M. Trivisano, C. Luisi et al., "Epileptic seizures, movement disorders, and breathing disturbances in Rett syndrome: diagnostic relevance of video-polygraphy," Epilepsy and Behavior, vol. 25, no. 3, pp. 401-407, 2012.

[25] E. E. Hagebeuk, R. P. Bijlmer, J. H. Koelman et al., "Respiratory disturbances in Rett syndrome: don't forget to evaluate upper airway obstruction," Journal of Child Neurology, vol. 27, no. 7, pp. 888-892, 2012.

[26] M. Carotenuto, M. Esposito, A. D'Aniello et al., "Polysomnographic findings in Rett syndrome: a case-control study," Sleep and Breathing, vol. 17, no. 1, pp. 93-98, 2013.

[27] J. Gallego, "Genetic diseases: congenital central hypoventilation, Rett, and Prader-Willi syndromes," Comprehensive Physiology, vol. 2, no. 3, pp. 2255-2279, 2012.

[28] N. Voituron, C. Menuet, M. Dutschmann, and G. Hilaire, "Physiological definition of upper airway obstructions in mouse model for Rett syndrome," Respiratory Physiology & Neurobiology, vol. 173, no. 2, pp. 146-156, 2010.

[29] C. De Felice, L. Ciccoli, S. Leoncini et al., "Systemic oxidative stress in classic Rett syndrome," Free Radical Biology and Medicine, vol. 47, no. 4, pp. 440-448, 2009.

[30] C. De Felice, C. Signorini, T. Durand et al., "F2-dihomoisoprostanes as potential early biomarkers of lipid oxidative damage in Rett syndrome," Journal of Lipid Research, vol. 52, no. 12, pp. 2287-2297, 2011.

[31] S. Leoncini, C. De Felice, C. Signorini et al., "Oxidative stress in Rett syndrome: natural history, genotype, and variants," Redox Report, vol. 16, no. 4, pp. 145-153, 2011.

[32] A. Pecorelli, L. Ciccoli, C. Signorini et al., "Increased levels of 4HNE-protein plasma adducts in Rett syndrome," Clinical Biochemistry, vol. 44, no. 5-6, pp. 368-371, 2011.

[33] C. Signorini, C. De Felice, S. Leoncini et al., "F4-neuroprostanes mediate neurological severity in Rett syndrome," Clinica Chimica Acta, vol. 412, no. 15-16, pp. 1399-1406, 2011.

[34] T. Durand, C. De Felice, C. Signorini et al., "F(2)-Dihomoisoprostanes and brain white matter damage in stage 1 Rett syndrome," Biochimie, vol. 95, no. 1, pp. 86-90, 2013.

[35] C. De Felice, C. Signorini, S. Leoncini et al., "The role of oxidative stress in Rett syndrome: an overview," Annals of the New York Academy of Sciences, vol. 1259, pp. 121-135, 2012.

[36] C. Sticozzi, G. Belmonte, A. Pecorelli et al., "Scavenger receptor B1 post-translational modifications in Rett syndrome," FEBS Letters, vol. 587, no. 14, pp. 2199-2204, 2013.

[37] E. Grillo, C. Lo Rizzo, L. Bianciardi et al., "Revealing the complexity of a monogenic disease: Rett syndrome exome sequencing," PLoS ONE, vol. 8, no. 2, Article ID e56599, 2013.

[38] C. De Felice, G. Guazzi, M. Rossi et al., "Unrecognized lung disease in classic Rett syndrome: a physiologic and high-resolution CT imaging study," Chest, vol. 138, no. 2, pp. 386-392, 2010.

[39] W. D. Travis, U. Costabel, D. M. Hansell et al., "ATS/ERS Committee on Idiopathic Interstitial Pneumonias. An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias," American Journal of Respiratory and Critical Care Medicine, vol. 188, no. 6, pp. 733-748, 2013.

[40] A. Caminati, A. Cavazza, N. Sverzellati et al., "An integrated approach in the diagnosis of smoking-related interstitial lung diseases," European Respiratory Review, vol. 21, no. 125, pp. 207-217, 2012.

[41] L. Colvin, S. Fyfe, S. Leonard et al., "Describing the phenotype in Rett syndrome using a population database," Archives of Disease in Childhood, vol. 88, no. 1, pp. 38-43, 2003.

[42] D. C. Tarquinio, K. J. Motil, W. Hou et al., "Growth failure and outcome in Rett syndrome: specific growth references," Neurology, vol. 79, no. 16, pp. 1653-1661, 2012.

[43] C. Signorini, M. Comporti, and G. Giorgi, "Ion trap tandem mass spectrometric determination of F2-isoprostanes," Journal of Mass Spectrometry, vol. 38, no. 10, pp. 1067-1074, 2003.

[44] A. Meister, "Glutathione-ascorbic acid antioxidant system in animals," The Journal of Biological Chemistry, vol. 269, no. 13, pp. 9397-9400, 1994.

[45] F. Q. Schafer and G. R. Buettner, "Redox environment of the cell as viewed through the redox state of the glutathione disulfide/glutathione couple," Free Radical Biology and Medicine, vol. 30, no. 11, pp. 1191-1212, 2001.

[46] I. Dalle-Donne, R. Rossi, R. Colombo, D. Giustarini, and A. Milzani, "Biomarkers of oxidative damage in human disease," Clinical Chemistry, vol. 52, no. 4, pp. 601-623, 2006.

[47] F. Tietze, "Enzymic method for quantitative determination of nanogram amounts of total and oxidized glutathione: applications to mammalian blood and other tissues," Analytical Biochemistry, vol. 27, no. 3, pp. 502-522, 1969.

[48] M. A. Baker, G. J. Cerniglia, and A. Zaman, "Microtiter plate assay for the measurement of glutathione and glutathione disulfide in large numbers of biological samples," Analytical Biochemistry, vol. 190, no. 2, pp. 360-365, 1990.

[49] C. Iber, S. Ancoli-Israel, A. L. Chesson, and S. F. Quan, "The AASM Manual for the Scoring of Sleep and Associated Events," American Academy of Sleep Medicine, 2007

[50] Section on Pediatric Pulmonology and Subcommittee on Obstructive Sleep Apnea Syndrome, "Clinical practice guideline: diagnosis and management of childhood obstructive sleep apnea syndrome," Pediatrics, vol. 109, no. 4, pp. 704-712, 2002.

[51] J. B. West, "Ventilation-perfusion inequality and overall gas exchange in computer models of the lung," Respiration Physiology, vol. 7, no. 1, pp. 88-110, 1969.

[52] C. A. Sorbini, V. Grassi, E. Solinas, and G. Muiesan, "Arterial oxygen tension in relation to age in healthy subjects," Respiration, vol. 25, no. 1, pp. 3-13, 1968.

[53] R. Z. Chen, S. Akbarian, M. Tudor, and R. Jaenisch, "Deficiency of methyl-CpG binding protein-2 in CNS neurons results in a Rett-like phenotype in mice," Nature Genetics, vol. 27, no. 3, pp. 327-331, 2001.

[54] D. P Wolfer, W. E. Crusio, and H.-P. Lipp, "Knockout mice: simple solutions to the problems of genetic background and flanking genes," Trends in Neurosciences, vol. 25, no. 7, pp. 336-340, 2002.

[55] A. Cortelazzo, C. De Felice, R. Guerranti et al., "Subclinical inflammatory status in Rett syndrome," Mediators of Inflammation, vol. 2013, Article ID 480980, 13 pages, 2013.

[56] J. B. West and P. D. Wagner, "Pulmonary gas exchange," American Journal of Respiratory and Critical Care Medicine, vol. 157, no. 4, pp. S82-S87, 1998.

[57] C. L. Marcus, "Pathophysiology of childhood obstructive sleep apnea: current concepts," Respiration Physiology, vol. 119, no. 23, pp. 143-154, 2000.

[58] S. Powell, H. Kubba, C. O'Brien, and M. Tremlett, "Paediatric obstructive sleep apnoea," British Medical Journal, vol. 340, p. 1918, 2010.

[59] C. L. Marcus, L. J. Brooks, and K. A. Draper, "American Academy of Pediatrics. Diagnosis and management of childhood obstructive sleep apnea syndrome," Pediatrics, vol. 130, no. 3, pp. 576-584, 2012.

[60] N. J. Ali, D. J. Pitson, and J. R. Stradling, "Snoring, sleep disturbance, and behaviour in 4-5 year olds," Archives of Disease in Childhood, vol. 68, no. 3, pp. 360-366, 1993.

[61] L. Brunetti, S. Rana, M. L. Lospalluti et al., "Prevalence of obstructive sleep apnea syndrome in a cohort of 1,207 children of Southern Italy," Chest, vol. 120, no. 6, pp. 1930-1935, 2001.

[62] E. O. Bixler, A. N. Vgontzas, H.-M. Lin et al., "Sleep disordered breathing in children in a general population sample: prevalence and risk factors," Sleep, vol. 32, no. 6, pp. 731-736, 2009.

[63] T. Young, M. Palta, J. Dempsey, J. Skatrud, S. Weber, and S. Badr, "The occurrence of sleep-disordered breathing among middle-aged adults," The New England Journal of Medicine, vol. 328, no. 17, pp. 1230-1235, 1993.

[64] E. O. Bixler, A. N. Vgontzas, T. Ten Have, K. Tyson, and A. Kales, "Effects of age on sleep apnea in men. I. Prevalence and severity," American Journal of Respiratory and Critical Care Medicine, vol. 157, no. 1, pp. 144-148, 1998.

[65] E. O. Bixler, A. N. Vgontzas, H.-M. Lin et al., "Prevalence of sleep-disordered breathing in women: effects of gender," American Journal of Respiratory and Critical Care Medicine, vol. 163, no. 3 I, pp. 608-613, 2001.

[66] L. Dyugovskaya, P. Lavie, and L. Lavie, "Increased adhesion molecules expression and production of reactive oxygen species in leukocytes of sleep apnea patients," American Journal of Respiratory and Critical Care Medicine, vol. 165, no. 7, pp. 934-939, 2002.

[67] L. Lavie, A. Vishnevsky, and P. Lavie, "Evidence for lipid peroxidation in obstructive sleep apnea," Sleep, vol. 27, no. 1, pp. 123-128, 2004.

[68] M. Quintero, C. Gonzalez-Martin, and V. Vega-Agapito, "The effects of intermittent hypoxia on redox status, NF-[kappa]B activation, and plasma lipid levels are dependent on the lowest oxygen saturation," Free Radical Biology and Medicine, vol. 65, pp. 1143-1154, 2013.

[69] I. G. Kang, J. H. Jung, and S. T. Kim, "The effect of obstructive sleep apnea on DNA damage and oxidative stress," Clinical & Experimental Otorhinolaryngology, vol. 6, no. 2, pp. 68-72, 2013.

[70] J. R. Skelly, S. C. Rowan, J. F. Jones et al., "Upper airway dilator muscle weakness following intermittent and sustained hypoxia in the rat: effects of a superoxide scavenger," Physiological Research, vol. 62, no. 2, pp. 187-196, 2013.

[71] M. Ntalapascha, D. Makris, and A. Kyparos, "Oxidative stress in patients with obstructive sleep apnea syndrome," Sleep Breath, vol. 17, no. 2, pp. 549-555, 2013.

[72] M. Mancuso, E. Bonanni, A. LoGerfo et al., "Oxidative stress biomarkers in patients with untreated obstructive sleep apnea syndrome," Sleep Medicine, vol. 13, no. 6, pp. 632-636, 2012.

[73] P. Celec, J. Hodosy, M. Behuliak et al., "Oxidative and carbonyl stress in patients with obstructive sleep apnea treated with continuous positive airway pressure," Sleep and Breathing, pp. 393-398, 2011.

[74] K. Katsoulis, T. Kontakiotis, D. Spanogiannis et al., "Total antioxidant status in patients with obstructive sleep apnea without comorbidities: the role of the severity of the disease," Sleep and Breathing, vol. 15, no. 4, pp. 861-866, 2011.

[75] S. Cofta, E. Wysocka, and T. Piorunek, "Oxidative stress markers in the blood of persons with different stages of obstructive sleep apnea syndrome," Journal of Physiology and Pharmacology, vol. 59, no. 6, pp. 183-190, 2008.

[76] M. Simiakakis, F. Kapsimalis, and E. Chaligiannis, "Lack of effect of sleep apnea on oxidative stress in obstructive sleep apnea syndrome (OSAS) patients," PLoS ONE, vol. 7, no. 6, Article ID e39172, 2013.

[77] B. Halliwell and J. M. C. Gutteridge, "Oxygen toxicity, oxygen radicals, transition metals and disease," Biochemical Journal, vol. 219, no. 1, pp. 1-14, 1984.

[78] L. Ciccoli, V. Rossi, S. Leoncini et al., "Iron release in erythrocytes and plasma non protein-bound iron in hypoxic and non hypoxic newborns," Free Radical Research, vol. 37, no. 1, pp. 51-58, 2003.

[79] L. Ciccoli, V. Rossi, S. Leoncini et al., "Iron release, superoxide production and binding of autologous IgG to band 3 dimers in newborn and adult erythrocytes exposed to hypoxia and hypoxia-reoxygenation," Biochimica et Biophysica Acta, vol. 1672, no. 3, pp. 203-213, 2004.

[80] E. Nagababu, S. Ramasamy, and J. M. Rifkind, "Site-specific cross-linking of human and bovine hemoglobins differentially alters oxygen binding and redox side reactions producing rhombic heme and heme degradation," Biochemistry, vol. 41, no. 23, pp. 7407-7415, 2002.

[81] L. Ciccoli, C. De Felice, E. Paccagnini et al., "Morphological changes and oxidative damage in Rett Syndrome erythrocytes," Biochimica et Biophysica Acta, vol. 1820, no. 4, pp. 511-520, 2012.

[82] G. Bianciardi, M. Acampa, I. Lamberti et al., "Microvascular abnormalities in Rett syndrome," Clinical Hemorheology and Microcirculation, vol. 54, no. 1, pp. 109-113, 2013.

[83] A. Panighini, E. Duranti, F. Santini et al., "Vascular dysfunction in a mouse model of Rett syndrome and effects of curcumin treatment," PLoS ONE, vol. 8, no. 5, Article ID E64863, 2013.

[84] E. Cardaioli, M. T. Dotti, and G. Hayek, "Studies on mitochondrial pathogenesis of Rett syndrome: ultrastructural data from skin and muscle biopsies and mutational analysis at mtDNA nucleotides 10463 and 2835," Journal of Submicroscopic Cytology and Pathology, vol. 31, no. 2, pp. 301-304, 1999.

[85] E. Grosser, U. Hirt, and O. A. Janc, "Oxidative burden and mitochondrial dysfunction in a mouse model of Rett syndrome," Neurobiology of Disease, vol. 48, no. 1, pp. 102-114, 2012.

[86] S. Kriaucionis, A. Paterson, J. Curtis, J. Guy, N. MacLeod, and A. Bird, "Gene expression analysis exposes mitochondrial abnormalities in a mouse model of Rett syndrome," Molecular and Cellular Biology, vol. 26, no. 13, pp. 5033-5042, 2006.

[87] A. Pecorelli, G. Leoni, F. Cervellati et al., "Genes related to mitochondrial ATP production, protein degradation and chromatin folding are differentially expressed in lymphomonocytes of Rett syndrome patients," Mediators of Inflammation, vol. 2013, Article ID 137629, 18 pages, 2013.

[88] C. De Felice, S. Maffei, C. Signorini et al., "Subclinical myocardial dysfunction in Rett syndrome," European Heart Journal Cardiovascular Imaging, vol. 13, no. 4, pp. 339-345, 2012.

[89] J. M. Bissonnette and S. J. Knopp, "Separate respiratory phenotypes in methyl-CpG-binding protein 2 (Mecp2) deficient mice," Pediatric Research, vol. 59, no. 4, pp. 513-518, 2006.

[90] M. Ogier and D. M. Katz, "Breathing dysfunction in Rett syndrome: understanding epigenetic regulation of the respiratory network," Respiratory Physiology and Neurobiology, vol. 164, no. 1-2, pp. 55-63, 2008.

[91] D. M. Katz, M. Dutschmann, J.-M. Ramirez, and G. Hilaire, "Breathing disorders in Rett syndrome: progressive neurochemical dysfunction in the respiratory network after birth," Respiratory Physiology & Neurobiology, vol. 168, no. 1-2, pp. 101-108, 2009.

[92] R. Z. Chen, S. Akbarian, M. Tudor, and R. Jaenisch, "Deficiency of methyl-CpG binding protein-2 in CNS neurons results in a Rett-like phenotype in mice," Nature Genetics, vol. 27, no. 3, pp. 327-331, 2001.

[93] M. Ogier, H. Wang, E. Hong, Q. Wang, M. E. Greenberg, and D. M. Katz, "Brain-derived neurotrophic factor expression and respiratory function improve after ampakine treatment in a mouse model of Rett syndrome," The Journal of Neuroscience, vol. 27, no. 40, pp. 10912-10917, 2007.

[94] J. Guy, B. Hendrich, M. Holmes, J. E. Martin, and A. Bird, "A mouse Mecp2-null mutation causes neurological symptoms that mimic Rett syndrome," Nature Genetics, vol. 27, no. 3, pp. 322-326, 2001.

[95] J.-C. Viemari, J.-C. Roux, A. K. Tryba et al., "Mecp2 deficiency disrupts norepinephrine and respiratory systems in mice," The Journal of Neuroscience, vol. 25, no. 50, pp. 11521-11530, 2005.

[96] G. M. Stettner, P. Huppke, C. Brendel, D. W. Richter, J. Gartner, and M. Dutschmann, "Breathing dysfunctions associated with impaired control of postinspiratory activity in Mecp2-/y knock-out mice," Journal of Physiology, vol. 579, no. 3, pp. 863-876, 2007.

[97] A. P. L. Abdala, M. Dutschmann, J. M. Bissonnette, and J. F. R. Paton, "Correction of respiratory disorders in a mouse model of Rett syndrome," Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 42, pp. 18208-18213, 2010.

[98] G. Song, C. Tin, E. Giacometti, and C.-S. Poon, "Habituation without NMDA receptor-dependent desensitization of Hering-Breuer apnea reflex in a Mecp2+/- mutant mouse model of Rett syndrome," Frontiers in Integrative Neuroscience, vol. 2, no. 5, p. 6, 2011.

[99] S. Gonnelli, C. Caffarelli, J. Hayek et al., "Bone ultrasonography at phalanxes in patients with Rett syndrome: a 3-year longitudinal study," Bone, vol. 42, no. 4, pp. 737-742, 2008.

[100] M. Acampa and F. Guideri, "Cardiac disease and Rett syndrome," Archives of Disease in Childhood, vol. 91, no. 5, pp. 440-443, 2006.

[101] K. J. Motil, E. Caeg, J. O. Barrish et al., "Gastrointestinal and nutritional problems occur frequently throughout life in girls and women with Rett syndrome," Journal of Pediatric Gastroenterology and Nutrition, vol. 55, no. 3, pp. 292-298, 2012.

[102] N. C. Derecki, E. Privman, and J. Kipnis, "Rett syndrome and other autism spectrum disordersbrain diseases of immune malfunction," Molecular Psychiatry, vol. 15, no. 4, pp. 355-363, 2010.

[103] N. C. Derecki, J. C. Cronk, Z. Lu et al., "Wild-type microglia arrest pathology in a mouse model of Rett syndrome," Nature, vol. 484, no. 7392, pp. 105-109, 2012.

Claudio De Felice, (1) Marcello Rossi, (2) Silvia Leoncini, (3, 4) Glauco Chisci, (5) Cinzia Signorini, (3) Giuseppina Lonetti, (6) Laura Vannuccini, (2) Donatella Spina, (7) Alessandro Ginori, (7) Ingrid Iacona, (2) Alessio Cortelazzo, (4, 8) Alessandra Pecorelli, (3, 4) Giuseppe Valacchi, (9) Lucia Ciccoli, (3) Tommaso Pizzorusso, (6, 10) and Joussef Hayek (4)

(1) Neonatal Intensive Care Unit, University Hospital Azienda Ospedaliera Universitaria Senese (AOUS), Viale M. Bracci 16, 53100 Siena, Italy

(2) Respiratory Pathophysiology and Rehabilitation Unit, University Hospital, AOUS, Viale M. Bracci 16, 53100 Siena, Italy

(3) Department of Molecular and Developmental Medicine, University of Siena, Via A. Moro 2, 53100 Siena, Italy

(4) Child Neuropsychiatry Unit, University Hospital AOUS, Viale M. Bracci 16, 53100 Siena, Italy

(5) Department of Maxillofacial Surgery, University of Siena, Viale M. Bracci 16, 53100 Siena, Italy

(6) Institute of Neuroscience, CNR, Via G. Moruzzi 1, 56124 Pisa, Italy

(7) Pathology Unit, University Hospital AOUS, Viale M. Bracci 16, 53100 Siena, Italy

(8) Department of Medical Biotechnologies, University of Siena, Via A. Moro 2, 53100 Siena, Italy

(9) Department of Life Sciences and Biotechnology, University of Ferrara, Via Borsari 46, 44100 Ferrara, Italy

(10) Department of Neuroscience, Psychology, Drug Research and Child Health (Neurofarba), University of Florence, Area S. Salvi Pad. 26, 50135 Florence, Italy

Correspondence should be addressed to Marcello Rossi; m.rossi@ao-siena.toscana.it

Received 11 October 2013; Revised 6 January 2014; Accepted 14 January 2014; Published 17 March 2014

Academic Editor: Paul Ashwood

TABLE 1: Relevant demographic and clinical characteristics of female
subjects with Rett syndrome.

Variables Patients (N)                             228
Age (years)                           12.9 [+ or -] 7.9 ([section])
Body weight (RTT z-score                   0.025 [+ or -] 1.12
  for age) (1)
Body height (RTT z-score                   -0.05 [+ or -] 1.13
  for age) (1)
Head circumference (RTT z-                 -0.22 [+ or -] 1.11
  score) (1)
Body mass index (BMI) (RTT z-score         -0.36 [+ or -] 1.45
  for age) (l)
Clinical severity score (CSS) (2)           17.4 [+ or -] 7.3
Tachypneaa Respiratory dysfunction              59 (25.9%)
    on a clinical basis (b)
  +                                             42 (18.4%)
  ++                                           127 (55.7%)
  +++                                           59 (25.9%)
Additional clinical features
  Air-S (c)                                     64 (28.1%)
  Severe GERD (d)                               27 (11.8%)

(1) Calculated z-scores for age are referred to a validated Rett
syndrome-specific growth charts [42]. (2) Clinical severity score
was defined according to Neul et al., 2008 [7]. (a) Tachypnea was
defined as a respiratory rate > 1.8 times (i.e., above the upper
quartile) of the expected respiratory rate for age and gender;
brespiratory dysfunction was categorized based on the
corresponding Percy's clinical severity scale item [41];
([section]) mean [+ or -] SD; (c) Air-S: abnormal air swallowing;
(d) GERD: gastroesophageal reflux disease.

TABLE 2: Pulmonary gas exchange abnormality (GEA) in patients with
typical Rett syndrome (n = 228): relationships between lung
ventilation-perfusion (V/Q) inequalities patterns and respiratory
variables.

Variables                  Pulmonary ventilation/
                         perfusion (V/Q) patterns in
                            typical Rett syndrome

                            No mismatch (N = 44)

Lot (L/min)                6.74 [+ or -] 2.87# (a)

Respiratory rate              27.4 [+ or -] 6.9
(breaths/min)

Alveolar vent. (L/         4.22 [+ or -] 2.60# (a)
min)

Pa[O.sub.2] (mmHg)        95.4 [+ or -] 15.2# (a,b)

Std. Pa[O.sub.2]             87.6 [+ or -] 14.4
(mmHg) *

PaC[O.sub.2] (mmHg)           35.3 [+ or -] 8.5

Blood pH                     7.429 [+ or -] 0.05

(A-a) [O.sub.2]        14.1 [+ or -] 7.9# (a,b,c,d,e)
(mmHg)

Bohr's DS %              45.0 [+ or -] 19.4# (a,c,d)
[V.sub.t]

[Q.sub.s]/[Q.sub.t]           6.3 [+ or -] 2.9
(%)

[MATHEMATICAL             137.8 [+ or -] 63.5# (a)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

[MATHEMATICAL                 148 [+ or -]# 78
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

Respiratory ratio          1.12 [+ or -] 0.43 (a)

Variables                  Pulmonary ventilation/
                        perfusion (V/Q) patterns in
                           typical Rett syndrome

                               "Low" (N = 64)

Lot (L/min)              5.26 [+ or -] 2.08# (a,b)

Respiratory rate             25.9 [+ or -] 9.1
(breaths/min)

Alveolar vent. (L/       3.14 [+ or -] 1.80# (a,b)
min)

Pa[O.sub.2] (mmHg)       85.7 [+ or -] 15.7# (a,d)

Std. Pa[O.sub.2]             81.2 [+ or -] 14.9
(mmHg) *

PaC[O.sub.2] (mmHg)          36.3 [+ or -] 7.9

Blood pH                    7.436 [+ or -] 0.05

(A-a) [O.sub.2]         25.1 [+ or -] 11.3# (a,b,d)
(mmHg)

Bohr's DS %             41.2 [+ or -] 15.7# (b,c,d)
[V.sub.t]

[Q.sub.s]/[Q.sub.t]          24.3 [+ or -] 2.9
(%)

[MATHEMATICAL          89.5 [+ or -] 58.3# (a,b,t,d)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

[MATHEMATICAL              126 [+ or -] 58# (a,e)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

Respiratory ratio       1.72 [+ or -] 0.77 (a,b,c,d)

Variables                  Pulmonary ventilation/
                        perfusion (V/Q) patterns in
                           typical Rett syndrome

                              "High" (N = 73)

Lot (L/min)             9.71 [+ or -] 5.60# (b,c,d)

Respiratory rate             30.4 [+ or -] 8.0
(breaths/min)

Alveolar vent. (L/     6.93 [+ or -] 4.80# (a,b,c,d)
min)

Pa[O.sub.2] (mmHg)       92.2 [+ or -] 12.4# (c,d)

Std. Pa[O.sub.2]             87.3 [+ or -] 11.2
(mmHg) *

PaC[O.sub.2] (mmHg)          37.0 [+ or -] 6.6

Blood pH                    7.417 [+ or -] 0.04

(A-a) [O.sub.2]         27.8 [+ or -] 10.8# (a,c,d)
(mmHg)

Bohr's DS %               55.4 [+ or -] 10.8# (c)
[V.sub.t]

[Q.sub.s]/[Q.sub.t]           8.3 [+ or -] 7.2
(%)

[MATHEMATICAL            181 [+ or -] 132# (b,c,d)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

[MATHEMATICAL             193 [+ or -] 146# (a,b)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

Respiratory ratio          1.09 [+ or -] 0.30 (b)

Variables                   Pulmonary ventilation/
                         perfusion (V/Q) patterns in
                            typical Rett syndrome

                               "Mixed" (N = 36)

Lot (L/min)                6.07 [+ or -] 2.46# (c)

Respiratory rate              26.7 [+ or -] 8.3
(breaths/min)

Alveolar vent. (L/          4.01 [+ or -] 2.2# (c)
min)

Pa[O.sub.2] (mmHg)        83.9 [+ or -] 16.9# (b,c)

Std. Pa[O.sub.2]              78.7 [+ or -] 20.3
(mmHg) *

PaC[O.sub.2] (mmHg)           36.9 [+ or -] 6.9

Blood pH                     7.422 [+ or -] 0.04

(A-a) [O.sub.2]        36.9 [+ or -] 10.8# (a,b,c,d,e)
(mmHg)

Bohr's DS %              54.7 [+ or -] 15.5# (a,b,d)
[V.sub.t]

[Q.sub.s]/[Q.sub.t]           19.4 [+ or -] 8.9
(%)

[MATHEMATICAL             88.6 [+ or -] 29.7# (d,e)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

[MATHEMATICAL               117 [+ or -] 55# (b,e)
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

Respiratory ratio           1.37 [+ or -] 0.59 (c)

Variables                 Pulmonary ventilation/       P value
                        perfusion (V/Q) patterns in    (ANOVA)
                           typical Rett syndrome

                        "Simple" Mismatch (N = 11)

Lot (L/min)                 63 [+ or -] 14# (d)        < 0.001#

Respiratory rate             27.1 [+ or -] 9.0          0.162
(breaths/min)

Alveolar vent. (L/        4.23 [+ or -] 1.30# (d)      < 0.001#
min)

Pa[O.sub.2] (mmHg)          89.8 [+ or -]# 3.6          0.025#

Std. Pa[O.sub.2]             88.8 [+ or -] 6.5          0.082
(mmHg) *

PaC[O.sub.2] (mmHg)          39.4 [+ or -] 3.7          0.753

Blood pH                    7.413 [+ or -] 0.01         0.462

(A-a) [O.sub.2]        19.4 [+ or -] 2.6# (b,c,d,e)    < 0.001#
(mmHg)

Bohr's DS %             41.8 [+ or -] 9.5# (a,c,d)     < 0.001#
[V.sub.t]

[Q.sub.s]/[Q.sub.t]         21.6 [+ or -] 15.1          0.224
(%)

[MATHEMATICAL            164.8 [+ or -] 20.8# (e)      < 0.001#
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

[MATHEMATICAL              174 [+ or -] 59# (e)         0.015#
EXPRESSION NOT
REPRODUCIBLE IN
ASCII] (mL/min)

Respiratory ratio         1.04 [+ or -] 0.25 (d)       < 0.001#

Data are expressed as means [+ or -] SD. Bold characters indicate
statistical significant differences; superscript letters indicate
significant pairwise post hoc differences; Vtot: total
ventilation; (A-a) [0.sub.2]: Bohr's DS: physiological dead
space, as calculated by the Bohr equation, which, by quantifying
the ratio of physiological dead space to the total tidal volume
([V.sub.d]-[V.sub.t] = PaC[0.sub.2]-PaC[0.sub.2]-PaC[0.sub.2]),
gives an indication of the extent of wasted ventilation; (A-a)
[0.sub.2]: [0.sub.2] alveolar-arterial gradient; Vt: tidal
volume; [Q.sub.s]-[Q.sub.t]: pulmonary functional shunting;
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]: oxygen
uptake; [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]:
carbon dioxide production; * values were calculated according to
the formula by Sorbini et al. accounting for hypocapnia: standard
Pa[0.sub.2] = 1.66 x PaC[0.sub.2] + Pa[0.sub.2]-66.4 [52];
respiratory ratio: respiratory exchange ratio, that is,
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. See text for
further methodology details.

Note: Thestatistical significant differences are indicated with #.

TABLE 3: Redox/antioxidant status in patients with typical Rett
syndrome: systemic oxidative stress with decreased GSH/GSSG
ratio.

Redox and                 Rett syndrome         Healthy controls
antioxidant                 (n = 228)               (n = 114)
markers

P-NPBI (nmol/mL)       0.90 [+ or -] 0.18      0.43 [+ or -] 0.25
IE-NPBI (nmol/mL)      1.20 [+ or -] 0.30      0.78 [+ or -] 0.17
F2-IsoPs (pg/mL)       70.1 [+ or -] 20.5      24.2 [+ or -] 11.5
GSH ([micro]mol/L)    1673.0 [+ or -] 591.0   1165.0 [+ or -] 132.0
GSSG ([micro]mol/L)    179.0 [+ or -] 73.9     3.55 [+ or -] 1.90
GSH to GSSG ratio       10.9 [+ or -] 5.5      160.0 [+ or -] 61.0

Redox and             P value
antioxidant
markers

P-NPBI (nmol/mL)      < 0.0001
IE-NPBI (nmol/mL)     < 0.0001
F2-IsoPs (pg/mL)      < 0.0001
GSH ([micro]mol/L)    < 0.0001
GSSG ([micro]mol/L)   < 0.0001
GSH to GSSG ratio     < 0.0001

P-NPBI: plasma non-protein-bound iron; IE-NPBI: intraerythrocyte
non-protein-bound iron; [F.sub.2]-IsoPs: plasma [F.sub.2]-isoprostanes;
GSH: reduced glutathione; GSSG: oxidized
glutathione.

TABLE 4: Results of cardiorespiratory monitoring in patients with
typical Rett syndrome (n = 228) confirming a high frequency of
apneas and hypopneas either during wakefulness or sleep states.

Recorded events         Median    Interquartile
                       events/h       range

Sleep
  Obstructive apneas     6.2         3.4-58
  Central apneas         0.4        0.15-0.92
  Mixed apneas           1.5         0.4-2.5
  Hypopneas              25.6       20.1-34.7
Wakefulness
  Obstructive apneas     17.7       4.9-11.38
  Central apneas         0.7        0.08-1.07
  Mixed apneas           1.7        0.92-2.4
  Hypopneas               22         12.7-26

Apnoeas were defined as a > 90% airflow decrease for [greater
than or equal to] 10 sec; hypopnoeas were defined as a > 50%
airflow reduction for [greater than or equal to] 10 sec
associated with a decrease of [greater than or equal to] 3% in
oxygen saturation [51]. Obstructive apneas refer to recorded
events with cessation of airflow for [greater than or equal to]
10 sec associated with persistent respiratory effort; central
apneas refer to events characterized by cessation of airflow for
[greater than or equal to] 10 sec without associated respiratory
effort; mixed apneas refer to respiratory events that begin as
central apneas and end up as obstructive apneas.

TABLE 5: Correlation matrix between redox/antioxidant status and
severity of recorded apnoeas, either during wakefulness or sleep,
in patients with typical Rett syndrome (N = 228).

Redox and                    Apnoeas
antioxidant
markers             Mild     Moderate    Severe

P-NPBI             0.185      0.176      0.265#
                  (0.1431)   (0.1631)   (0.0346)#

IE-NPBI            0.324#     0.373#     0.358#
                  (0.0089)#  (0.0024)#  (0.0037)#

[F.sub.2]-IsoPs    0.2220     0.225      0.305#
                  (0.0800)   (0.0744)   (0.0142)#

GSH                0.252#     0.101      0.183
                  (0.0449)#  (0.4262)   (0.1485)

GSSG               0.258#     0.260#     0.267#
                  (0.0392)#  (0.0378)#  (0.033)#

GSH/GSSG ratio     -0.210     -0.247#    -0.241
                  (0.0961)   (0.0488)#  (0.0552)

Data are expressed as rank correlation rho coefficients with P
values in brackets. Bold characters indicate statistically
significant associations. Apnoeas were defined as a > 90% airflow
decrease for -10 sec; hypopneas were defined as a > 50% airflow
reduction for [greater than or equal to] 10 sec associated with a
decrease of [greater than or equal to] 3% in oxygen saturation
[50]. Apnoeas were further categorized as mild (10 to 15 sec),
moderate (15 to 30 sec), and severe (> 30 sec) on the basis of
their recorded duration. Legends: P-NPBI: plasma non-protein-
bound iron; IE-NPBI: intraerythrocyte non-protein-bound iron;
[F.sub.2]-IsoPs: plasma [F.sub.2]-isoprostanes; GSH: reduced
glutathione; GSSG: oxidized glutathione.

Note: Statistically significant associations is indicated with #.

TABLE 6: Frequency-severity of apneas, recorded during either the
wakefulness or sleep, identifies Rett patients with increased
intraerythrocyte non-protein-bound iron (IE-NPBI) levels:
receiver operating characteristic (ROC) curves analyses.

Variable               AUC [+ or -] SE         95% C.I.     E-value

Total apneas/h      0 690 [+ or -] 0 0669#   0.563-0.799#   0.0044#
Mild apneas/h       0.634 [+ or -] 0.0714    0.504-0.751    0.0605
Moderate apneas/h   0.664 [+ or -] 0.0687#   0.563-0.778#   0.0163#
Severe apneas/h     0 670 [+ or -] 0 0693#   0.541-0.782#   0.0142#

Variable            Criterion   Sens.%   Spec.%    +LR     -LR     +PV

Total apneas/h       > 7.4#     53.8#    82.05#   1.5#    0.89#   56.2#
Mild apneas/h          > 1       30.7     89.5    1.54    0.51    51.4
Moderate apneas/h    > 0.8#     80.7#    55.3#    1.81#   0.35#   55.3#
Severe apneas/h       > 2#       50#     81.6#    2.71#   0.61#    65#

Variable             -PV

Total apneas/h      62.7#
Mild apneas/h       74.1
Moderate apneas/h   80.8#
Severe apneas/h     70.5#

AUC: area under the curve; SE: standard error; Sens.:
sensitivity; Spec: specificity; +LR: positive likelihood ratio; -
LR: negative likelihood ratio; +PV: positive predictive value; -
PV: negative predictive value. Bold characters indicate
statistically significant items.

Note: Statistically significant items are indicated with #.

TABLE 7: Relationships between lung ventilation/perfusion (V/Q)
patterns and the redox/antioxidant status in patients with
typical Rett syndrome (n = 228).

Redox and                 Pulmonary ventilation/
antioxidant markers           perfusion (V/Q)
                            patterns in typical
                               Rett syndrome

                           No mismatch (N = 44)

P-NPBI (nmol/mL)          0.50 [+ or -] 0.32# (a)
IE-NPBI (nmol/mL)         0.80 [+ or -] 0.24# (a)
[F.sub.2]-IsoPs (pg/mL)   27.3 [+ or -] 11.1# (a)
GSH ([micro]mol/L)        1206 [+ or -] 140# (a)
GSSG ([micro]mol/L)        8.0 [+ or -] 3.4# (a)
GSH/GSSG ratio             175 [+ or -] 83# (a)

Redox and                   Pulmonary ventilation/
antioxidant markers             perfusion (V/Q)
                              patterns in typical
                                 Rett syndrome

                                "Low" (N = 64)

P-NPBI (nmol/mL)          0.86 [+ or -] 0.07# (a,b,c)
IE-NPBI (nmol/mL)          1.04 [+ or -] 0.05# (a,b)
[F.sub.2]-IsoPs (pg/mL)    65.2 [+ or -] 14.4# (a,b)
GSH ([micro]mol/L)         1867 [+ or -] 759# (a,b)
GSSG ([micro]mol/L)       193.6 [+ or -] 85.3# (a,b)
GSH/GSSG ratio              12.2 [+ or -] 7.8# (a)

Redox and                   Pulmonary ventilation/
antioxidant markers            perfusion (V/Q)
                             patterns in typical
                                Rett syndrome

                               "High" (N = 73)

P-NPBI (nmol/mL)            0.91 [+ or -] 015# (a)
IE-NPBI (nmol/mL)         1.20 [+ or -] 0.21# (a,b)
[F.sub.2]-IsoPs (pg/mL)   76.5 [+ or -] 13.2# (a,b)
GSH ([micro]mol/L)         1794 [+ or -] 507# (a,b)
GSSG ([micro]mol/L)       222.5 [+ or -] 61.5# (a,b)
GSH/GSSG ratio              8.2 [+ or -] 1.9# (a)

Redox and                   Pulmonary ventilation/
antioxidant markers             perfusion (V/Q)
                              patterns in typical
                                 Rett syndrome

                               "Mixed" (N = 36)

P-NPBI (nmol/mL)          1.02 [+ or -] 0.22# (a,b,c)
IE-NPBI (nmol/mL)          1.30 [+ or -] 0.49# (a,b)
[F.sub.2]-IsoPs (pg/mL)    100.8 [+ or -] 114# (a,b)
GSH ([micro]mol/L)         1442 [+ or -] 373# (a,b)
GSSG ([micro]mol/L)         132.0 [+ or -] 25.9 (a)
GSH/GSSG ratio                 116 [+ or -] 51a

Redox and                   Pulmonary ventilation/     P value
antioxidant markers            perfusion (V/Q)         (ANOVA)
                             patterns in typical
                                Rett syndrome

                          "Simple" mismatch (V = 11)

P-NPBI (nmol/mL)          0.71 [+ or -] 0.05# (b,c)    < 0.001
IE-NPBI (nmol/mL)           0.96 [+ or -] 0.13 (b)     < 0.001
[F.sub.2]-IsoPs (pg/mL)     46.2 [+ or -] 7.9 (b)      < 0.001
GSH ([micro]mol/L)          1419 [+ or -] 523 (b)      < 0.001
GSSG ([micro]mol/L)        144.3 [+ or -] 71.4 (b)     < 0.001
GSH/GSSG ratio              11.1 [+ or -] 5.2 (a)      < 0.001

Data are expressed as means [+ or -] SD. Bold characters indicate
statistical significant differences; superscript letters indicate
significant pairwise post hoc differences; P-NPBI: plasma non-
protein-bound iron; IE-NPBI: intraerythrocyte non-protein-bound
iron; [F.sub.2]-IsoPs: plasma [F.sub.2]-isoprostanes; GSH:
reduced glutathione; GSSG: oxidized glutathione.

Note: Statistical significant differences are indicated with #.
COPYRIGHT 2014 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2014 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Clinical Study
Author:De Felice, Claudio; Rossi, Marcello; Leoncini, Silvia; Chisci, Glauco; Signorini, Cinzia; Lonetti, G
Publication:Mediators of Inflammation
Date:Jan 1, 2014
Words:11981
Previous Article:IL-21 modulates release of proinflammatory cytokines in LPS-stimulated macrophages through distinct signaling pathways.
Next Article:Usefulness of adalimumab in the treatment of refractory uveitis associated with juvenile idiopathic arthritis.
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters