Severe Sleep Apnea-hypopnea Syndrome Research Articles

Association between adherence to the mediterranean diet, physical activity, and sleep apnea-hypopnea syndrome (SAHS) in a middle-aged population with cardiovascular risk: Insights from the ILERVAS cohort

IntroductionSleep Apnea-Hypopnea Syndrome (SAHS) is a common sleep disorder influenced by factors like age, gender, and obesity. The Mediterranean Diet (MedDiet) and physical activity have shown health benefits in lung diseases, but their effects on SAHS remain underexplored. MethodsIn a cross-sectional analysis of 678 middle-aged individuals with low-to-moderate cardiovascular risk from the ILERVAS cohort, we assessed adherence to the MedDiet and physical activity levels using validated tools. Sleep parameters, SAHS severity, and excessive daytime sleepiness were evaluated through non-attended cardiorespiratory polygraphy and the Epworth Sleepiness Scale. Multinomial logistic regression models were employed to assess the relationship between MedDiet adherence, physical activity, and SAHS severity. ResultsThe prevalence of severe, moderate, and mild SAHS was 15.5%, 23.2% and 36.1%, respectively. We found no significant associations between adherence to the MedDiet, physical activity levels, and the presence or severity of SAHS. However, we noted a significant interaction between MedDiet and physical activity with minimum SpO2 values (p = 0.049). Notably, consuming more than one serving of red meat per day was independently associated with a higher risk of moderate SAHS [OR = 2.65 (1.29–5.44), p = 0.008]. ConclusionIndividually, MedDiet adherence and physical activity did not show independent correlations with SAHS. However, when considered together, a minimal but significant effect on minimum SpO2 was observed. Additionally, red meat consumption was associated with a moderate risk of SAHS. Further research is necessary to comprehend the intricate connections between lifestyle factors and sleep-breathing disorders, with a focus on personalized approaches for high-risk populations.

Contribution of central sleep apnea to severe sleep apnea hypopnea syndrome

PurposeCentral sleep apnea (CSA) is usually distinguished from obstructive sleep apnea (OSA). In fact, CSA is often a component of severe sleep apnea hypopnea syndrome (SAHS), rather than occurring alone. We investigated the clinical characteristics and polysomnography (PSG) parameters of CSA components in patients with severe SAHS.MethodsThe clinical characteristics and PSG parameters were retrospectively analyzed.ResultsPure or dominant CSA was rare (5% of all patients). Of all patients with CSA, 72% also exhibited other apnea subtypes that contributed to severe SAHS. Among patients with severe SAHS, those with CSA were more likely than others to be older; thinner; exhibit higher prevalences of comorbid coronary heart disease, arrhythmia, and heart failure; a higher apnea/hypopnea index (AHI); mixed apnea index (MAI); an elevated oxygen desaturation index (ODI); and more nighttime oxygen saturation levels < 90%. Multivariate logistic regression analysis revealed that older age, comorbid arrhythmia or heart failure, and an elevated ODI were independently associated with CSA.ConclusionPatients who complain of snoring or apnea may be better evaluated by comprehensive PSG prior to treatment if they are old, show greater hypoxia, or suffer from arrhythmia and/or heart failure, because such patients are more likely than others to exhibit CSA.

Effects of obstructive sleep apnea hypopnea syndrome on cardiac function in patients with chronic obstructive pulmonary disease

Objective: To analyze the clinical characteristics of patients with chronic obstructive pulmonary disease (COPD) and COPD overlapping obstructive sleep apnea hypopnea syndrome (overlap syndrome), and to study the relationship between overlap syndrome and cardiovascular diseases. Methods: A total of 126 stable COPD patients admitted to the Respiratory Department of Peking University Third Hospital from September 2016 to October 2018 were included in this study, including 112 males and 14 females, ranging in age from 48 to 89 years, with a median of 67 years. With apnea hypopnea index (AHI) 5 times/h for the cutoff value, we classified the patients into a simple COPD group (31 cases) and an overlap syndrome group (95 cases), and compared the patients' demographic characteristics, respiratory symptoms, lung function, the incidence of cardiovascular events and the cardiac function with echocardiography (E/e'), left atrium diameter (LAD) and left ventricular ejection fraction (LVEF), by using independent-samples T test and chi-square test. Results: There were no statistically significant differences in demographic characteristics, respiratory symptoms, pulmonary function, cardiac function between COPD patients and overlap syndrome patients, but significant differences in blood oxygen level at night and left ventricular mass index(LVMI) between these groups (P=0.014,P<0.001,P<0.001,P<0.001, P=0.047, respectively) were observed. By comparing the severe sleep apnea hypopnea syndrome (AHI≥30) with sleep apnea hypopnea syndrome patients(AHI<30), there were statistically significant differences in echocardiographic indicators, among which there were statistically significant differences in E/e'(P=0.013), LAD(P=0.006), LVMI (P=0.051) and LVEF (P=0.030).There were also significant differences in the history of coronary heart disease and congestive heart failure between the two groups (P=0.025, P<0.001). After dividing the patients with overlap syndrome by mild, moderate and severe severity, E/e' and LAD were significantly correlated with severity (P=0.045, P=0.011). In terms of blood oxygen level at night, there was a significant correlation between average blood oxygen saturation at night and E/e', LAD, and LVMI (r=-0.195, P=0.033; r=-0.197, P=0.030; r=-0.195, P=0.044); moreover, there was also a significant correlation between the ratio of blood oxygen≤90% and LAD (r=0.209, P=0.021). In the multiple linear regression model, E/e' increased by 0.070 on average for each unit increase in AHI, and 0.084 on average for each unit increase in oxygen desaturation index (ODI). Conclusions: Patients with COPD overlapping severe sleep apnea hypopnea syndrome showed worse left diastolic function and higher risk of congestive heart failure and coronary heart disease compared with the patients with COPD alone. In addition, the degree of impairment of left heart diastolic function was associated with the severity of COPD overlapping sleep apnea hypopnea syndrome. The higher the AHI and the ODI became, the more severe the left heart diastolic restriction and structures changed.

Influence of body mass index and obesity upon the association between periodontitis and sleep apnea-hypopnea syndrome.

We aimed to explore the association between periodontitis and sleep apnea-hypopnea syndrome (SAHS) and the possible influence of body mass index (BMI) and obesity upon this association. A case-control study was made involving 114 subjects (60 patients with SAHS and 54 controls). A thorough periodontal evaluation was carried out, and demographic and clinical data were collected. Periodontitis was more prevalent in SAHS (80%) than in the controls (48.1%). The recorded statistically significant association (OR = 4.31; p = 0.001) was seen to weaken in the multivariate model (OR = 2.03; p = 0.204), with BMI adopting a more influential role. The apnea-hypopnea index (AHI) was correlated to probing depth (PD) (r = 0.40; p = 0.002) and clinical attachment level (CAL) (r = 0.41; p = 0.001). The periodontal parameters were seen to be higher in obese SAHS patients (BMI ≥ 30kg/m2) than in non-obese SAHS patients (BMI < 30kg/m2). The greater prevalence and severity of periodontitis in patients with SAHS were fundamentally influenced by BMI. On the other hand, periodontitis was more severe in patients with severe SAHS. Patients with SAHS have a greater prevalence of periodontitis, fundamentally related to increased obesity. Therefore, obese subjects with SAHS should be screened for periodontal disease.

Usefulness of Home Overnight Pulse Oximetry in Patients with Suspected Sleep-Disordered Breathing.

Methods Prospective study conducted in a university hospital. Subjects with a clinical suspicion of SAHS were included. All of them underwent home polygraphy and oximetry on the same night. A correlation was made between the apnea-hypopnea index (AHI) and the oximetry variables. The variable with the highest diagnostic value was calculated using the area under the curve (AUC), and the best cut-off point for discriminating between patients with SAHS and severe SAHS was identified. Results One hundred and four subjects were included; 73 were men (70%); mean age was 52 ± 10.1 years; body mass index was 30 ± 4.1, and AHI = 29 ± 23.2/h. A correlation was observed between the AHI and oximetry variables, particularly ODI3 (r = 0.850; P < 0.001) and ODI4 (r = 0.912; P < 0.001). For an AHI ≥ 10/h, the ODI3 had an AUC = 0.941 (95% confidence interval (CI) = 0.899–0.982) and the ODI4, an AUC = 0.984 (95% CI = 0.964–1), with the ODI4 having the best cut-off point (5.4/h). Similarly, for an AHI ≥ 30/h, the ODI4 had an AUC = 0.922 (95% CI = 0.859–0.986), with the best cut-off point being 10.5/h. Conclusion Nocturnal oximetry is useful for diagnosing and evaluating the severity of SAHS. The ODI4 variable was most closely correlated with AHI for both diagnosis.

Xerostomia in patients with sleep apnea-hypopnea syndrome: A prospective case-control study.

BackgroundTo describe the characteristics of xerostomia (dry mouth) in the population with sleep apnea-hypopnea syndrome (SAHS) and contrast its prevalence versus that found in healthy subjects, and to compare the frequency of xerostomia in SAHS patients with and without continuous positive airway pressure (CPAP) treatment. Material and MethodsA prospective comparative study was made between adults recently diagnosed with SAHS in a public hospital (n=60) and healthy individuals (n=54). The presence of xerostomia was assessed on waking up and during the day, using a frequency scale from 0 (“never”) to 3 (“always”). ResultsThe prevalence of xerostomia on waking up in the SAHS group was 45%, versus 20.4% among the controls. During the rest of the day the prevalence of the symptom decreased in both the SAHS group (21.7%) and among the controls (9.3%). Multiple binary logistic regression analysis found body mass index (BMI) to be correlated to the association SAHS-xerostomia on waking up (p=0.007). Patients with moderate SAHS had a greater frequency of xerostomia than those with mild SAHS (p=0.022). The frequency of xerostomia on waking up was significantly greater in patients using CPAP than in those without such treatment (57.1% versus 16.7%, respectively) (p=0.008). ConclusionsThe frequency of xerostomia was greater in patients with SAHS, particularly on waking up and in those receiving CPAP. The symptom was more prevalent in individuals with moderate to severe SAHS than in those with mild SAHS, and was significantly influenced by BMI. Key words:Sleep apnea, xerostomia, dry mouth, CPAP, side effects, body mass index, obesity.

Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis

Background and objectiveIn-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. ConclusionsRP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.

Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis.

The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.

Increased Urinary Erythropoietin Excretion in Severe Sleep Apnea–Hipoapnea Syndrome: The Effect of CPAP

IntroductionTissue hypoxia stimulates the production of erythropoietin (EPO), the main effect of which is, in turn, to stimulate erythropoiesis. Sleep apnea–hypopnea syndrome (SAHS) is an entity characterized by repeated episodes of hypoxemia during sleep. ObjectiveTo analyze whether hypoxemia stimulated increased urinary excretion of EPO, and if so, to evaluate if treatment with continuous positive airway pressure (CPAP) can inhibit this phenomenon. MethodsWe studied 25 subjects with suspected SAHS who underwent a polysomnography study (PSG). EPO levels in first morning urine (uEPO) and blood creatinine and hemoglobin were determined in all patients. Patients with severe SAHS repeated the same determinations after CPAP treatment. ResultsTwelve subjects were diagnosed with severe SAHS (mean±SD, AHI 53.1±22.7). Creatinine and hemoglobin levels were normal in all subjects. uEPO was 4 times higher in the SAHS group than in the control group (1.32±0.83 vs 0.32±0.35 UI/l, P<.002). CPAP treatment reduced uEPO to 0.61±0.9UI/l (P<.02), levels close to those observed in healthy subjects. No dose–response relationship was observed between severity of PSG changes and uEPO values. ConclusionsPatients with severe SAHS show increased uEPO excretion, but this normalizes after treatment with CPAP.

Association between Sleep Apnea Hypopnea Syndrome and the Risk of Atrial Fibrillation: A Meta-Analysis of Cohort Study.

Numerous reports have been done to seek the relationship between sleep apnea hypopnea syndrome (SAHS) and the risk of atrial fibrillation (AF). However, definite conclusion has not yet been fully established. We examined whether SAHS increases AF incidence in common population and summarized all existing studies in a meta-analysis. We summarized the current studies by searching related database for potential papers of the association between SAHS and the risk of AF. Studies that reported original data or relative risks (RRs) with 95% confidence intervals (CIs) for the associations were included. Sensitivity analyses were performed by omitting each study iteratively and publication bias was detected by Begg's tests. Eight eligible studies met the inclusion criteria. Fixed effects meta-analysis showed that SAHS increased AF risk in the common population (RR = 1.70, 95% CI: 1.53–1.89, P = 0.002, I2 = 69.2%). There was a significant association between mild SAHS and the risk of AF (RR = 1.52, 95% CI: 1.28–1.79, P = 0.01, I2 = 78.4%), moderate SAHS (RR = 1.88, 95% CI: 1.55–2.27, P = 0.017, I2 = 75.6%), and severe SAHS (RR = 2.16, 95% CI: 1.78–2.62, P < 0.001, I2 = 91.0%). The results suggest that sleep apnea hypopnea syndrome could increase the risk of AF, and the higher the severity of SAHS, the higher risk of atrial fibrillation.

The Impact of Respiratory Events on the Autonomic Nervous System during Sleep.

Sleep apnea hypopnea syndrome (SAHS) is an independent risk factor for cardiovascular diseases. However, the pathophysiology between them is not yet clear. This paper seeks to understand how respiratory events impact the cardiovascular system by heart rate variability. We compared the differences between successional pathological respiratory events (PR) and pure normal respiration (NR) during sleep. The transitions between normal and pathological respiration (TR) were also analyzed. Thirteen patients who suffered moderate or severe SAHS were enrolled in this study. The results demonstrate that the beat-to-beat interval (RR interval) mean value and sample entropy are significantly lower during PR than during NR. RR interval standard deviation, the power of very low frequency (VLF) and low frequency (LF), total power, and the low frequency/high frequency (LF/HF) ratio were significantly larger during PR than during NR. However, the high frequency (HF) power was not significantly different between normal and pathological respiration. Additionally, the trends during TR also supported these significant differences. The results indicate that during pathological respiration, as the heart rate and its volatility increase, the complexity of its rhythm decreases. We conclude that the energy of the autonomic nervous system rapidly increases during pathological respiration, especially at the beginning. The HF power does not significantly change to modulate the heart rhythm, but the activity of the sympathetic nervous system will significantly increase, resulting in the imbalance of the LF/HF ratio. In addition to these findings, this paper discusses the influence of arousal on these indices during TR.

Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings

Background and objectiveThe aim of this study was to assess the utility of bispectrum-based oximetry approaches as a complementary tool to traditional techniques in the screening of pediatric sleep apnea-hypopnea syndrome (SAHS). Methods298 blood oxygen saturation (SpO2) signals from children ranging 0–13 years of age were recorded during overnight polysomnography (PSG). These recordings were divided into three severity groups according to the PSG-derived apnea hypopnea index (AHI): AHI < 5 events per hour (e/h), 5 ≤ AHI < 10 e/h, AHI ≥ 10 e/h. For each pediatric subject, anthropometric variables, 3% oxygen desaturation index (ODI3) and spectral features from power spectral density (PSD) and bispectrum were obtained. Then, the fast correlation-based filter (FCBF) was applied to select a subset of relevant features that may be complementary, excluding those that are redundant. The selected features fed a multiclass multi-layer perceptron (MLP) neural network to build a model to estimate the SAHS severity degrees. ResultsAn optimum subset with features from all the proposed methodological approaches was obtained: variables from bispectrum, as well as PSD, ODI3, Age, and Sex. In the 3-class classification task, the MLP model trained with these features achieved an accuracy of 76.0% and a Cohen's kappa of 0.56 in an independent test set. Additionally, high accuracies were reached using the AHI cutoffs for diagnosis of moderate (AHI = 5 e/h) and severe (AHI = 10 e/h) SAHS: 81.3% and 85.3%, respectively. These results outperformed the diagnostic ability of a MLP model built without using bispectral features. ConclusionsOur results suggest that bispectrum provides additional information to anthropometric variables, ODI3 and PSD regarding characterization of changes in the SpO2 signal caused by respiratory events. Thus, oximetry bispectrum can be a useful tool to provide complementary information for screening of moderate-to-severe pediatric SAHS.

El síndrome de apneas-hipoapneas del sueño (SAHS) grave incrementa la excreción urinaria de eritropoyetina. Efecto del tratamiento con CPAP

IntroductionTissue hypoxia stimulates the production of erythropoietin (EPO), the main effect of which is, in turn, to stimulate erythropoiesis. Sleep apnea-hypopnea syndrome (SAHS) is an entity characterized by repeated episodes of hypoxemia during sleep. ObjectiveTo analyze whether hypoxemia stimulated increased urinary excretion of EPO, and if so, to evaluate if treatment with continuous positive airway pressure (CPAP) can inhibit this phenomenon. MethodsWe studied 25 subjects with suspected SAHS who underwent a polysomnography study (PSG). EPO levels in first morning urine (uEPO) and blood creatinine and hemoglobin were determined in all patients. Patients with severe SAHS repeated the same determinations after CPAP treatment. ResultsTwelve subjects were diagnosed with severe SAHS (mean ± SD, AHI 53.1 ± 22.7). Creatinine and hemoglobin levels were normal in all subjects. uEPO was 4 times higher in the SAHS group than in the control group (1.32 ± 0.83 vs. 0.32 ± 0.35 UI/l, p <.002). CPAP treatment reduced uEPO to 0.61 ± 0.9 UI/l (p <.02), levels close to those observed in healthy subjects. No dose-response relationship was observed between severity of PSG changes and uEPO values. ConclusionsPatients with severe SAHS show increased uEPO excretion, but this normalizes after treatment with CPAP.

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment.

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac processη) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to theη,δ,θ,α,σ,βtime series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP forδandθbut not forη The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Long-term Impact of Continuous Positive Airway Pressure Therapy on Arrhythmia and Heart Rate Variability in Patients With Sleep Apnea

IntroductionAutonomic dysfunction can alter heart rate variability and increase the incidence of arrhythmia. We analyzed the impact of continuous positive airway pressure (CPAP) on this pathophysiological phenomenon in patients with severe sleep apnea–hypopnea syndrome. MethodsConsecutive patients with recently diagnosed severe sleep apnea–hypopnea syndrome were prospectively considered for inclusion. Incidence of arrhythmia and heart rate variability (recorded on a 24-h Holter monitoring device) were analyzed before starting CPAP therapy and 1 year thereafter. ResultsA total of 26 patients were included in the study. CPAP was administered for 6.6±1.8h during Holter monitoring. After starting CPAP, we observed a marginally significant reduction in mean HR (80±9 to 77±11bpm, P=.05). CPAP was associated with partial modulation (only during waking hours) of r-MSSD (P=.047) and HF (P=.025) parasympathetic parameters and LF (P=.049) sympathetic modulation parameters. None of these parameters returned completely to normal levels (P<.001). The number of unsustained episodes of atrial tachycardia diminished (P=.024), but no clear effect on other arrhythmias was observed. ConclusionsCPAP therapy only partially improves heart rate variability, and exclusively during waking hours, and reduces incidence of atrial tachycardia, both of which can influence cardiovascular morbidity and mortality in sleep apnea–hypopnea syndrome patients.

Value of actigraphy and oximetry for diagnosing sleep apnea-hypopnea syndrome

To explore the value of actigraphy and oximetry for diagnosing sleep apnea-hypopnea syndrome (SAHS). Suspected SAHS subjects were enrolled from sleep center of Peking University People's Hospital between December 2013 and September 2014. Light-out and light-on were simultaneously recorded for polysomnography (PSG), actigraphy and oximetry. Actigraphy was used to correct total sleep time (TST) for oximetry. Oxygen desaturation index (ODI4), namely the times of pulse oxygen saturation (SpO2) drop≥4% per hour, was detected by oximetry. ODIA was used instead of ODI4 with TST corrected by actigraphy. Receiver operating characteristic (ROC) curve was used for evaluating the value of ODI4 and ODIA for diagnosing SAHS and cut-off value was calculated. Paired t-test or signed rank t-test was used for data acquired using actigraphy or oximetry compared with data acquired by PSG. Sensitivity and specificity were calculated using fourfold table. Disease severity of SAHS was classified by apnea-hypopnea index (AHI) detected by PSG. If AHI<5/h, SAHS was not considered. 5-<15/h was classified as mild, 15-<30/h was classified as moderate and AHI≥30/h was classified as severe. Among 213 SAHS subjects, 38 of them were normal, 34 of them were mild, 51 were moderate and 90 were severe. Compared with PSG, actigraphy can correctly estimate TST in non-SAHS (P=0.408) and mild SAHS groups (P=0.949); while in moderate and severe SAHS groups, TSTs detected by actigraphy were shorter than TSTs acquired by PSG ((405±51) vs (419±40) min and (399 ± 62) vs (422 ± 60) min) (both P<0.05). Area under ROC curve (AUC) of ODI4 for diagnosing SAHS was 0.956 using a cut-off value of 5; the AUC of ODIA for diagnosing SAHS was 0.951 with a cut-off value of 5 as well. The sensitivity of ODI4 for mild, moderate and severe SAHS was 80.6%, 66.7% and 58.9% respectively, all with a specificity of 100%. After adjusting TST with actigraphy, the sensitivity of ODIA for mild, moderate and severe SAHS was 84.0%, 73.8 % and 68.9%, with specificity of 94.7%, 100% and 99.2% respectively. Actigraphy can correctly estimate TST in non-SAHS and mild SAHS groups. ODI4 can be used for diagnosing SAHS. After correcting TST by actigraphy, the sensitivity of ODIA is higher.

Severe sleep apnea–hypopnea syndrome is related to left ventricle dysfunction and hypertrophy in acromegalic patients

Severe sleep apnea–hypopnea syndrome is related to left ventricle dysfunction and hypertrophy in acromegalic patients

Severe sleep apnea–hypopnea syndrome is related to left ventricle dysfunction and hypertrophy in acromegalic patients

ObjectiveTo assess whether sleep apnea-hypopnea syndrome (SAHS) is a risk factor for development of acromegalic cardiomyopathy. MethodsA descriptive, cross-sectional study of 32 patients with acromegaly (15 categorized as non-controlled-NCA and 17 as controlled-CA) compared to 20 matched controls (by sex, age, and BMI) referred to the pulmonology department for suspected SAHS. Polysomnography, echocardiography (M-mode, 2-dimensional, and Doppler), and 12-lead electrocardiography were performed in all participants. Development of cardiac morbidity (ischemia heart disease or heart failure) was evaluated after 7 years. ResultsSAHS was diagnosed in 81.3% of patients with acromegaly and 85% of controls. Mild SAHS was more common in CA than in NCA patients (31.3% vs. 0%, p=0.048). There was a trend to greater prevalence of left ventricular diastolic dysfunction (LVDD) in acromegalic patients as compared to controls (58.1% vs. 30%, p=0.05). Presence of severe SAHS in patients with acromegaly was related to greater risk of LVDD (90.9% vs. 40%, p=0.008; OR 2.3 [1.3–4.0]), LV hypertrophy (55.6% vs. 10.5%, p=0.02; OR 5.3 [1.3–22.2]), and cardiac events (87.5% vs. 35.6%; p=0.01; OR 7.53 [1.07–53.24]). ConclusionSAHS is highly prevalent in patients with acromegaly. Only in these patients was severe SAHS associated to hypertrophy, LV diastolic dysfunction, and cardiac events.

Impacto a largo plazo del tratamiento con presión positiva continua en la vía aérea superior sobre la incidencia de arritmias y la variabilidad de frecuencia cardiaca en pacientes con apnea del sueño

IntroductionAutonomic dysfunction can alter heart rate variability and increase the incidence of arrhythmia. We analyzed the impact of continuous positive airway pressure (CPAP) on this pathophysiological phenomenon in patients with severe sleep apnea-hypopnea syndrome. MethodsConsecutive patients with recently diagnosed severe sleep apnea-hypopnea syndrome were prospectively considered for inclusion. Incidence of arrhythmia and heart rate variability (recorded on a 24-hour Holter monitoring device) were analyzed before starting CPAP therapy and 1 year thereafter. ResultsA total of 26 patients were included in the study. CPAP was administered for 6.6±1.8hours during Holter monitoring. After starting CPAP, we observed a marginally significant reduction in mean HR (80±9 to 77±11 bpm, p=.05). CPAP was associated with partial modulation (only during waking hours) of r-MSSD (p=.047) and HF (p=.025) parasympathetic parameters and LF (p=.049) sympathetic modulation parameters. None of these parameters returned completely to normal levels (p<.001). The number of unsustained episodes of atrial tachycardia diminished (p=.024), but no clear effect on other arrhythmias was observed. ConclusionsCPAP therapy only partially improves heart rate variability, and exclusively during waking hours, and reduces incidence of atrial tachycardia, both of which can influence cardiovascular morbidity and mortality in sleep apnea-hypopnea syndrome patients.

Sleep Apnea–Hypopnea Syndrome and Type 2 Diabetes. A Reciprocal Relationship?

Epidemiological data suggest that sleep apnea–hypopnea syndrome (SAHS) is independently associated with the development of insulin resistance and glucose intolerance. Moreover, despite significant methodological limitations, some studies report a high prevalence of SAHS in patients with type 2 diabetes mellitus (DM2). A recent meta-analysis shows that moderate–severe SAHS is associated with an increased risk of DM2 (relative risk=1.63 [09–2.45]), compared to the absence of apneas and hypopneas.Common alterations in various pathogenic pathways add biological plausibility to this relationship. Intermittent hypoxia and sleep fragmentation, caused by successive apnea–hypopnea episodes, induce several intermediate disorders, such as activation of the sympathetic nervous system, oxidative stress, systemic inflammation, alterations in appetite-regulating hormones and activation of the hypothalamic–pituitary–adrenal axis which, in turn, favor the development of insulin resistance, its progression to glucose intolerance and, ultimately, to DM2.Concomitant SAHS seems to increase DM2 severity, since it worsens glycemic control and enhances the effects of atherosclerosis on the development of macrovascular complications. Furthermore, SAHS may be associated with the development of microvascular complications: retinopathy, nephropathy or diabetic neuropathy in particular. Data are still scant, but it seems that DM2 may also worsen SAHS progression, by increasing the collapsibility of the upper airway and the development of central apneas and hypopneas.