Sleepy Group Research Articles

Distinguishing between children referred for assessment of excessive daytime sleepiness using polysomnographic measures

BackgroundCurrently, diagnosing narcolepsy and idiopathic hypersomnia (IH) in subjectively sleepy children requires an overnight polysomnographic sleep study followed by a daytime multiple sleep latency test (MSLT). We aimed to compare sleep macro-architecture to identify differences between these groups. MethodsAll children referred for a MSLT between May 2010 to December 2023 whose parent consented for their data be used in research were eligible. Each child was age- and sex-matched to a control. Sleep stability was defined as the maintenance of a particular sleep stage before waking or transitioning to another sleep state. As a measure of sleep disturbance, the number and duration of bouts of each sleep stage was recorded. Results28 children with Narcolepsy, 11 with IH and 26 with subjective sleepiness were included. Children with narcolepsy exhibited higher numbers of transitions to wake after sleep onset compared to their controls and to the subjectively sleepy group (p < 0.001 for both). The number of REM bouts was greater in the narcolepsy group compared to their control group (p < 0.001), the IH group (p < 0.05) and the subjectively sleepy group (p < 0.05), while the average duration of REM bouts was shorter in the narcolepsy group compared to both the IH group (p < 0.05) and subjectively sleepy group (p < 0.05). Mean sleep latency on the MSLT was correlated with a number of polysomnographic variables. ConclusionsOur study suggests that specific sleep architecture patterns could potentially serve as diagnostic biomarkers for distinguishing paediatric narcolepsy from those with IH and those with a non-diagnostic MSLT.

Clinical characteristics of non-sleepy obstructive sleep apnea patients: a study in a tertiary care sleep clinic in India.

Obstructive sleep apnea (OSA) encompasses a diverse population, manifesting with or without symptoms of excessive daytime sleepiness. There is contention surrounding the significance of non-sleepy OSA within clinical contexts and whether routine treatment is warranted. This study aims to evaluate epidemiological and clinical distinctions between sleepy and non-sleepy OSA patients. A retrospective analysis was conducted on consecutive patients undergoing polysomnography for OSA assessment at tertiary care hospitals between 2018 and 2023. For 176 of 250 patients, complete polysomnography records with OSA diagnoses were available. Non-sleepy OSA was defined when a patient had an Epworth sleepiness scale score <10 and polysomnography demonstrated an apnea hypopnea index ≥5/hour. Non-sleepy OSA patients were matched with sleepy OSA patients in terms of age and gender distribution (mean age 51.24±13.25 years versus 50.9±10.87 years, male 70.4% versus 73.3%). The sensitivity of STOP-BANG≥3 for the non-sleepy OSA group was 87.7%, 89.3%, and 95.2% for any OSA severity, moderate to severe OSA, and severe OSA, respectively, while the corresponding sensitivity for the sleepy OSA group was 96.5%, 98.6%, and 100% for any OSA severity, moderate to severe OSA, and severe OSA, respectively. A novel symptom scoring tool, HASSUN (hypertension, nocturnal apneas, snoring, sleep disturbance, unrefreshing sleep, and nocturia), demonstrated a sensitivity of over 90% for all severity categories of OSA in both non-sleepy and sleepy OSA groups. The prevalence of cardiovascular and metabolic comorbidities did not significantly differ between non-sleepy and sleepy OSA patients. The physiological parameters, including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC ratio, arterial partial pressure of oxygen, and bicarbonate at baseline, were comparable between the two groups. To conclude, non-sleepy OSA patients are less obese, exhibit fewer symptoms, and have less severe OSA in comparison to sleepy OSA. Non-sleepy OSA patients display a similar likelihood of cardiovascular and metabolic comorbidities compared to sleepy OSA patients. Further investigations are warranted to elucidate the mechanisms underlying cardiovascular metabolic comorbidities in non-sleepy OSA patients. The proposed HASSUN scoring tool for non-sleepy OSA screening necessitates validation in future studies.

Heart rate variability analysis in obstructive sleep apnea patients with daytime sleepiness.

Recent studies suggest that sleepy patients with obstructive sleep apnea (OSA) are at higher risk for incident cardiovascular disease. This study assessed cardiac autonomic function in sleepy versus non-sleepy patients with OSA using heart rate variability (HRV) analysis. We hypothesized that HRV profiles of sleepy patients would indicate higher cardiovascular risk. Electrocardiograms (ECG) derived from polysomnograms (PSG) collected by the Sydney Sleep Biobank were used to study HRV in groups of sleepy (ESS ≥ 10) and non-sleepy OSA patients (ESS < 10). HRV parameters were averaged across available ECG signals during N2 sleep. A total of 421 patients were evaluated, with a mean age of 54 (14) years, body mass index of 33 (9) kg/m2, apnea-hypopnea index of 21 (28) events/h, and 66% male. The sleepy group consisted of 119 patients and the non-sleepy group 302 patients. Sleepy patients exhibited lower HRV values for: root mean square successive difference (RMSSD, p = 0.028), total power (TP, p = 0.031), absolute low frequency (LF, p = 0.045), and high-frequency (HF, p = 0.010) power compared to non-sleepy patients. Sleepy patients with moderate-to-severe OSA exhibited lower HRV values for: (RMSSD, p = 0.045; TP, p = 0.052), absolute LF (p = 0.051), and HF power (p = 0.025). There were no differences in other time and frequency domain HRV markers. This study shows a trend toward parasympathetic withdrawal in sleepy OSA patients, particularly in moderate-to-severe cases, lending mechanistic support to the link between the sleepy phenotype and CVD risk in OSA.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis.

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

Facilitators and barriers to getting obstructive sleep apnea diagnosed: perspectives from patients and their partners.

Obstructive sleep apnea (OSA) is a prevalent and debilitating condition that is significantly underdiagnosed. The majority of adults sleep with someone-a partner. Partners can play a significant role in the patient's OSA diagnosis. The goal of this work is to describe facilitators and barriers to OSA diagnosis as discussed by patients with OSA and their partners. This was a qualitative secondary analysis with results drawn from 20 dyadic interviews, conducted 1 couple at a time, in 20 newly diagnosed adult patients with OSA and their partners. Qualitative interview data were analyzed using conventional content analysis. Facilitators of OSA diagnosis were partners pushing patients to seek care, patients actively seeking care, and care providers identifying the patient's risk of OSA. Barriers to OSA diagnosis were patients' lack of serious attention to symptoms, patients' negative perceptual framing of diagnosis and treatment of OSA, and poor coordination of health care services. We recommend engaging partners in the OSA diagnosis and developing educational and behavioral interventions to raise public awareness about OSA. It is important to educate clinicians on atypical presentations of OSA. Further investigation is needed to evaluate the impact of health care services on OSA diagnosis. Ye L, Li W, Willis DG. Facilitators and barriers to getting obstructive sleep apnea diagnosed: perspectives from patients and their partners. J Clin Sleep Med. 2022;18(3):835-841.

How sleepy patients differ from non-sleepy patients in mild obstructive sleep apnea?

To aim is investigate whether demographic, polysomnographic or sleep behaviour data differ between non-sleepy, sleepy and very sleepy patients with mild obstructive sleep apnea. The study population consisted of 439 consecutive adult patients diagnosed with mild obstructive sleep apnea (5 ≤ apnea-hypopnea index < 15) after a complete polysomnographic evaluation. The patients were divided into three groups based on subjective sleepiness: very sleepy (Epworth Sleepiness Scale ≥ 16, n=59); sleepy (10 < Epworth Sleepiness Scale < 16, n=102); and non-sleepy (Epworth Sleepiness Scale ≤ 10, n=278). Demographic, polysomnographic and sleep behaviour data were compared between the groups. There were no statistically significant differences in breathing abnormality indices and most of the demographic features between the groups. The number of arousals was significantly higher in the very sleepy group compared with the non-sleepy group (140.8±105.2 versus 107.6±72.2). Very sleepy patients reported feeling sleepy during the daytime more often (42.4% versus 31.7%) and sleeping significantly less during the week compared with non-sleepy patients. Also, a significantly higher proportion of sleepy (47.1%) and very sleepy patients (44.1%) reported taking naps during weekends compared with non-sleepy patients (35.6%). In a regression analysis, also total sleep time (β=0.045), sleep efficiency (β=-0.160), apnea index (β=-0.397), apnea-hypopnea index in supine position (β=0.044), periodic limb movement index (β=0.196) and periodic limb movement-related arousal index (β=-0.210) affected subjective daytime sleepiness. The results suggest that excessive daytime sleepiness in patients with mild obstructive sleep apnea appears to be related to inadequate sleeping habits (i.e. insufficient sleep during working days) and decreased sleep quality rather than differences in breathing abnormalities.

Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence.

We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls. Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan-Meier survival curves for wake and sleep stages, and median bout durations. Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02). NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence.

Further Development of P4 Approach to Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a heterogeneous disorder. Cluster analysis has identified different physiologic subtypes with respect to symptoms. A difference exists in cardiovascular risk from OSA between the 7 subtypes identified. There are 3 basic subtypes replicated in multiple studies: (a) a group where insomnia is the main symptom; (b) an asymptomatic group; (c) a group with marked excessive sleepiness. The symptomatic benefit from treatment with nasal CPAP varies between these 3 subtypes. Data from the Sleep Heart Health Study reveal that the increased risk of cardiovascular disease from OSA occurs only in the excessively sleepy group.

Clinical and Polysomnographic Correlates of Subjective Sleepiness in Mild Obstructive Sleep Apnea.

The decision to treat mild obstructive sleep apnea (OSA) often hinges on the presence of subjective daytime sleepiness. This study was done to identify clinical and polysomnographic features which correlate with subjective sleepiness in mild OSA. Utilizing data from the Apnea Positive Pressure Long-term Efficacy Study, 199 participants with mild OSA were identified. Participants were grouped as "sleepy" or "non-sleepy" based on their responses to a question regarding excessive daytime sleepiness, and Epworth Sleepiness Scores. We compared demographic, clinical and baseline polysomnographic data between the groups. The prevalence of subjective sleepiness was 74.4%. The sleepy group was younger (46.1 ± 12.6 vs. 53.3 ± 13.1 years, p=0.001), reported lower quality of life (4.5 ± 0.69 vs. 4.9 ± 0.61, p=0.0002), had higher depression scores (5.4 ± 4.7 vs. 3.1 ± 3.5, p=0.003) and reported more naps per week (2.6 ± 2.9 vs. 1.3 ± 1.9, p=0.01). Total sleep time and sleep efficiency were notably higher in the sleepy (254.2 ± 106 vs. 220.4 ± 114 min, p=0.08) and (80.2 ± 12.6 vs. 75.7 ± 14.9 %, p=0.06), approaching statistical significance. The non-sleepy group had slightly higher apnea hypopnea index (AHI: 12.2 ± 1.5 vs. 11.2 ± 2.4 events/hour, p=0.01) and worse desaturation indices. Subjective sleepiness in mild OSA is associated with younger age, worsened mood and quality of life. This study suggests that evidence of increased sleep drive on polysomnography may correlate with subjective sleepiness in mild OSA.

White matter structural differences in OSA patients experiencing residual daytime sleepiness with high CPAP use: a non-Gaussian diffusion MRI study

ObjectivesTo investigate factors associated with residual sleepiness in patients who were highly adherent to continuous positive airway pressure (CPAP). Nocturnal inactivity, comorbidities, concomitant medications, and, in particular, white matter (WM) differences using diffusion magnetic resonance imaging (MRI) were explored using a continuous-time random-walk (CTRW) model. MethodsTwenty-seven male patients (30–55 years of age) with obstructive sleep apnea (OSA) received CPAP as the only treatment (CPAP ≥ 6 h/night) for at least 30 days. Based on the Psychomotor Vigilance Task (PVT) results, participants were divided into a non-sleepy group (lapses ≤ 5; n = 18) and a sleepy group (lapses > 5; n = 9). Mean nocturnal inactivity (sleep proxy) was measured using actigraphy for one week. Diffusion-weighted imaging (DWI) with high b-values, as well as diffusion tensor imaging (DTI), was performed on a 3 T MRI scanner. The DWI dataset was analyzed using the CTRW model that yielded three parameters: temporal diffusion heterogeneity α, spatial diffusion heterogeneity β, and an anomalous diffusion coefficient Dm. The differences in α, β, and Dm between the two groups were investigated by a whole-brain analysis using tract-based spatial statistics (TBSS), followed by a regional analysis on individual fiber tracts using a standard parcellation template. Results from the CTRW model were compared with those obtained from DTI. The three CTRW parameters were also correlated with the clinical assessment scores, Epworth Sleepiness Scale (ESS), PVT lapses, and PVT mean reaction time (MRT) in specific fiber tracts. ResultsThere were no differences between groups in mean sleep duration, comorbidities, and the number or type of medications, including alerting and sedating medications. In the whole-brain DWI analysis, the sleepy group showed higher α (17.27% of the WM voxels) and Dm (17.14%) when compared to the non-sleepy group (P < 0.05), whereas no significant difference in β was observed. In the regional fiber analysis, the sleepy and non-sleepy groups showed significant differences in α, β, or their combinations in a total of 12 fiber tracts; whereas similar differences were not observed in DTI parameters, when age was used as a covariate. Additionally, moderate to strong correlations between the CTRW parameters (α, β, or Dm) and the sleepiness assessment scores (ESS, PVT lapses, or PVT MRT) were observed in specific fiber tracts (|R| = 0.448–0.654, P = 0.0003–0.019). ConclusionsThe observed differences in the CTRW parameters between the two groups indicate that WM alterations can be a possible mechanism to explain reversible versus residual sleepiness observed in OSA patients with identical high level of CPAP use. The moderate to strong correlations between the CTRW parameters and the clinical scores suggest the possibility of developing objective and quantitative imaging markers to complement clinical assessment of OSA patients.

Recurrent On-Duty Sleepiness and Alertness Management Strategies in Long-Haul Airline Pilots.

We examined whether long-haul airline pilots without recurrent on-duty sleepiness obtain more prior sleep and use more effective in-flight alertness management strategies than their colleagues with recurrent on-duty sleepiness. There were 51 pilots who flew at least twice from Helsinki to Asia. Of them, 44 flew at least twice back to Helsinki following 1 local night. On-duty sleepiness was measured by the Karolinska Sleepiness Scale (KSS), alertness management strategies by a diary, and sleep by a diary and activity monitor. Pilots who rated KSS ≥ 7 on each, some, or none of the flights were classified as "regularly", "sometimes", and "never" sleepy, respectively. This classification was performed separately for the outbound and inbound flights. On the outbound flights, 22% of the pilots were "never", 54% "sometimes", and 24% "regularly" sleepy. For the inbound flights, the respective distribution was 25%, 48%, and 27%. Compared to the "regularly" sleepy group, the "never" sleepy group obtained 54 min more night sleep prior to the outbound flights. For the inbound flights, the respective difference was 1 h 23 min. Also, the "never" sleepy pilots slept 31 min more between days off than the "regularly" sleepy pilots. The results of the in-flight alertness management strategies were mixed. The study demonstrates that pilots without recurrent on-duty sleepiness obtain more sleep than their colleagues with recurrent on-duty sleepiness. The result emphasizes the need to investigate whether the sleep of recurrently sleepy pilots can be increased and whether this increase would reduce their on-duty sleepiness.Sallinen M, Åkerstedt T, Härmä M, Henelius A, Ketola K, Leinikka M, Kecklund G, Sihvola M, Tuori A, Virkkala J, Puttonen S. Recurrent on-duty sleepiness and alertness management strategies in long-haul airline pilots. Aerosp Med Hum Perform. 2018; 89(7):601-608.

Changing Faces of Obstructive Sleep Apnea: Treatment Effects by Cluster Designation in the Icelandic Sleep Apnea Cohort.

Distinct clinical phenotypes of obstructive sleep apnea (OSA) have been identified: Disturbed Sleep, Minimally Symptomatic, and Sleepy. Determining whether these phenotypes respond differently to standard treatment helps us to create a foundation for personalized therapies. We compared responses to positive airway pressure (PAP) therapy in these clinical OSA phenotypes. The study sample included 706 patients from the Icelandic Sleep Apnea Cohort with moderate-to-severe OSA who were prescribed PAP. Linear and logistic mixed models were used to compare 2-year changes in demographics, comorbid diseases, and sleep-related health issues within and across OSA clinical phenotypes. Relationships between changes in symptoms and PAP adherence were also examined. Overall, effect sizes were moderate to large when comparing sleepiness, insomnia-related, and apneic symptom changes in the Sleepy group with changes in other two groups, especially those in the Minimally Symptomatic group. Within the Disturbed Sleep group, PAP users and nonusers demonstrated similar changes in insomnia-related symptoms. The Minimally Symptomatic group remained relatively asymptomatic, but reported significant decreases in daytime sleepiness and physical fatigue; PAP users generally had larger improvements. The Sleepy group had reductions in nearly all measured symptoms, including large reductions in drowsy driving; almost all of these improvements were greater among PAP users than nonusers. OSA treatment response patterns differed by initial clinical phenotype and PAP adherence. Individuals with insomnia-related symptoms may require additional targeted therapy for these complaints. These findings underscore the need for a personalized approach to management that recognizes patients with a range of OSA presentations.

Brain white matter changes in CPAP-treated obstructive sleep apnea patients with residual sleepiness.

To investigate white matter (WM) structural alterations using diffusion tensor imaging (DTI) in obstructive sleep apnea (OSA) patients, with or without residual sleepiness, following adherent continuous positive airway pressure (CPAP) treatment. Possible quantitative relationships were explored between the DTI metrics and two clinical assessments of somnolence. Twenty-nine male patients (30-55 years old) with a confirmed diagnosis of OSA were recruited. The patients were treated with CPAP therapy only. The Psychomotor Vigilance Task (PVT) and Epworth Sleepiness Scale (ESS) were performed after CPAP treatment and additionally administered at the time of the magnetic resonance imaging (MRI) scan. Based on the PVT results, the patients were divided into a nonsleepy group (lapses ≤5) and a sleepy group (lapses >5). DTI was performed at 3T, followed by an analysis using tract-based spatial statistics (TBSS) to investigate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1 ), and radial diffusivity (λ23 ) between the two groups. A higher MD (P < 0.05) was observed in the sleepy group than the nonsleepy group in the whole-brain TBSS analysis in the WM. The increased MD (17.8% of the fiber tracts; P < 0.05) was caused primarily by an elevated λ23 . Axial diffusivity (λ1 ) exhibited no significant difference (P > 0.17). The alterations in FA or MD of individual fiber tracts occurred mainly in the internal/external capsule, corona radiata, corpus callosum, and sagittal stratum regions. The FA and MD values correlated with the PVT and ESS assessments from all patients (R ≥ 0.517, P < 0.05). Global and regional WM alterations, as revealed by DTI, can be a possible mechanism to explain why OSA patients with high levels of CPAP use can have differing responses to treatment. Compromised myelin sheath, indicated by increased radial diffusivity, can be involved in the underlying WM changes. Evidence level: 1 J. MAGN. RESON. IMAGING 2017;45:1371-1378.

PP.37.15

Objective: Obstructive sleep apnea (OSA) is an independent risk for cardiovascular disease. Apnea-hypopnea index (AHI) is a marker for the severity of OSA, and it also reflects the severity of cardiovascular damages. On the other hand, Epworth Sleepiness Scale (ESS) is a marker of clinical symptom of OSA (sleepiness), it has not been fully clarified ESS reflects the severity of cardiovascular damages. The present study was conducted to examine whether ESS is associated with the severity of cardiovascular damages independent of the severity of OSA. Design and method: In consecutive 1047 patients diagnosed as OSA (AHI>5) by polysomnography, ESS and brachial-ankle pulse wave velocity (baPWV) were measured. Logistic regression analysis of the ESS for baPWV was performed. Results: AHI was positively associated with baPWV. On the contrary, ESS was negatively associated with baPWV (Figure left panel). Multivariate regression analysis showed that ESS was significantly associated with baPWV independent of AHI. When patients were divided into two groups: non-sleepy OSA (ESS<10) group and sleepy OSA (ESS>11) group, baPWV (15.5 ± 0.4 vs. 14.3 ± 0.3 m/sec, p<0.001) was higher in the non-sleepy OSA group than those in sleepy OSA group (Figure right panel). These differences were significant even after the adjustments for confounding factors.Conclusions: Cardiovascular damages were prominent in patients with non-sleepy OSA than sleepy OSA. Thus, in addition to the severity of OSA, absence of sleepiness may be a risk for cardiovascular events in patients with OSA.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing.

AimThe present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset.MethodsTwo groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests.ResultsNon-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients.ConclusionThe regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

Sleepiness, inflammation and oxidative stress markers in middle-aged males with obstructive sleep apnea without metabolic syndrome: a cross-sectional study.

BackgroundThe simultaneous occurrence of metabolic syndrome and excessive daytime sleepiness are very common in obstructive sleep apnea (OSA) patients. Both conditions, if present in OSA, have been reported to be associated with inflammation and disruption of oxidative stress balance that impair the cardiovascular system. To verify the impact of daytime sleepiness on inflammatory and oxidative stress markers, we evaluated OSA patients without significant metabolic disturbance.MethodsThirty-five male subjects without diagnostic criteria for metabolic syndrome (Adult Treatment Panel III) were distributed into a control group (n = 10) (43 ± 10.56 years, apnea-hypopnea index - AHI 2.71 ± 1.48/hour), a non-sleepy OSA group (n = 11) (42.36 ± 9.48 years, AHI 29.48 ± 22.83/hour) and a sleepy OSA group (n = 14) (45.43 ± 10.06 years, AHI 38.20 ± 25.54/hour). Excessive daytime sleepiness was considered when Epworth sleepiness scale score was ≥ 10. Levels of high-sensitivity C-reactive protein, homocysteine and cysteine, and paraoxonase-1 activity and arylesterase activity of paraoxonase-1 were evaluated.ResultsPatients with OSA and excessive daytime sleepiness presented increased high-sensitivity C-reactive protein levels even after controlling for confounders. No significant differences were found among the groups in paraoxonase-1 activity nor arylesterase activity of paraoxonase-1. AHI was independently associated and excessive daytime sleepiness tended to have an association with high-sensitivity C-reactive protein.ConclusionsIn the absence of metabolic syndrome, increased inflammatory response was associated with AHI and daytime sleepiness, while OSA was not associated with abnormalities in oxidative stress markers.

Excessive daytime sleepiness in adult patients with ADHD as measured by the Maintenance of Wakefulness Test, an electrophysiologic measure.

To quantify the objective level of sleepiness in adult attention-deficit/hyperactivity disorder (ADHD) patients and to determine the relationship between excessive daytime sleepiness and simulated driving performance. Forty adult ADHD patients (DSM-IV criteria) and 19 matched healthy control subjects were included between June 30, 2010, and June 19, 2013. All participants completed the Epworth Sleepiness Scale and the Manchester Driving Behavior Questionnaire. After nocturnal polysomnography, they performed 2 neuropsychological tests, a 4 × 40-minute Maintenance of Wakefulness Test, and a 1-hour driving session. The primary outcome measure was the mean sleep latency on the Maintenance of Wakefulness Test. ADHD patients were divided into 3 groups defined by their Maintenance of Wakefulness Test scores. Participants (patients and control subjects) were allocated as follows: sleepy ADHD (0-19 min), intermediate ADHD (20-33 min), alert ADHD (34-40 min), and control group (34-40 min). The driving performance outcome was the mean standard deviation of lateral position of the vehicle during the simulated session. The group mean (SD) Epworth Sleepiness Scale score was higher in ADHD patients (12.1 [4.4]) than in controls (6.0 [2.7]) (P < .001). On the basis of the Maintenance of Wakefulness Test scores, 14 patients (35%) were in the sleepy group, 20 (50%) were in the intermediate group, and only 6 (15%) were in the alert group. Sleepy ADHD patients exhibited significantly deteriorated driving performance compared to the other 3 groups (P < .01). Our study shows that a significant proportion of adult ADHD patients exhibit an objective excessive daytime sleepiness, which, in addition, has an impact on simulated driving performance. Excessive daytime sleepiness, therefore, may be a key element needed to better evaluate these ADHD patients. ClinicalTrials.gov identifier: NCT01160874.

Excessive Daytime Sleepiness is Associated with Longer Culprit Lesion and Adverse Outcomes in Patients with Coronary Artery Disease

We assessed whether excessive daytime sleepiness was associated with coronary plaque phenotype and subsequent adverse cardiovascular events. Prospective cohort study. Intravascular ultrasound (IVUS) examination of the culprit coronary stenosis was performed. The Epworth Sleepiness Scale (ESS) questionnaire was administered, and the patients were divided into 2 groups-(1) sleepier and (2) less sleepy-based on the ESS score. Adverse cardiovascular outcomes were defined as cardiac death, myocardial infarction, stroke, unplanned revascularization, or heart failure admission. One hundred seventeen patients undergoing urgent or non-urgent coronary angiography were recruited. Compared with the less sleepy group (ESS ≤ 10, n = 87), the sleepier group (ESS > 10, n = 30) had higher serum levels of total cholesterol and of low-density-lipoprotein cholesterols (p < 0.05 for both). The IVUS examinations indicated coronary stenoses were longer in the sleepier group than in the less sleepy group (p = 0.011). The cumulative incidence of adverse cardiovascular events at 16-month follow-up was higher in the sleepier than the less sleepy group (12.5% versus 6.9%, p = 0.03). Cox regression analysis adjusting for age and smoking showed increased hazard of adverse cardiovascular events in sleepier group as compared to less sleepy group (HR = 3.44, 95% CI 1.01-11.72). In patients presenting with coronary artery disease, excessive daytime sleepiness based on ESS > 10 was associated with longer culprit lesions and future adverse cardiovascular events.

Heart rate variability during the MSLT and MWT in patients with and without excessive daytime sleepiness

Autonomic heart regulation changes during the transition from wakefulness to sleep. Heart rate variability (HRV) can be used to assess the autonomic nervous system function (ANS). We aimed to evaluate ANS in patients with and without excessive daytime sleepiness (EDS) during the maintenance of wakefulness test (MWT) and multiple sleep latency test (MSLT). From a series of 98 consecutive patients with symptoms of sleep disordered breathing (SDB) studied with 5 trials of MWT followed by a research version MSLT two groups of patients (matched by age and gender) were selected consecutively based on mean sleep latencies: the sleepy group (SG) with 17 patients ( MWT < 20 min and MSLT < 8 min) and the alert group (AG) with 20 patients (AG: MWT > 20 and MSLT > 8 min). For each test, the first 120-s window of RR signals during wakefulness was analyzed in the following frequency bands: VLF, < 0.04 Hz; LF, 0.04–0.15 Hz; HF, 0.15–0.4 Hz; TB, total-frequency band. ANS activity was described by measures obtained from traditional power spectral analysis (PSA) and from time–frequency representation (TFR). Non-linear measures – Correntropy (CORR) and auto-mutual-information function (AMIF) – were used to describe the RR regularity. Mean values from all day were compared between groups. Cohen’s effect size was calculated to measure the strength of each difference. Finally, a conditional forward logistic regression analysis was performed to find the best discriminatory variables between groups. TFR (in HF and VLF band), AMIF (in all bands) and CORR (in Total band) analysis showed that the SG had higher values than the AG ( p range 0.000–0.02) during the MSLT. The strongest differences were found in HF band from TRF and AMIF and in Total band from AMIF and CORR. The best sensitivity (82.4%) and specificity (85%) to distinguish patients with and without sleepiness were achieved with the combination of AMIF and CORR in Total band, respectively. No differences were found in traditional PSA measures. Heart rate variability analysis during the first 2 min of the MSLT while the patient is awake show specific changes in TFR and non-linear measures that differentiate sleepy from non-sleepy patients. Study supported by FIS PI07/0318 to MS, cofinanced by FEDER.

Sleepiness and driving performance in adults with Attention Deficit Hyperactivity Disorder (ADHD)

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by a triad of symptoms involving hyperactivity, impulsivity and inattention. Several studies in children with ADHD showed a high prevalence of excessive daytime sleepiness. To our knowledge, no study has objectively assessed sleepiness in adults with ADHD. Moreover, it has been shown that adults with ADHD were at risk for driving accidents. The objectives of this study are to quantify objective sleepiness and its impact on driving performance in adult with ADHD. 36 subjects with ADHD (age (mean ± SE) = 36.3 ± 1.6) and 18 control subjects (age (mean ± SE) = 31.2 ± 1.2) were included. Nocturnal polysomnography was performed to identify potential sleep disorder. The next day patients were submitted to a Maintenance Wakefulness Test (MWT) at 10H, 12H, 14H, 16H to examine their level of daytime sleepiness. After a training of 15 min, a driving test of 1 h was carried out at 17H on a simulator (Oktal) to evaluate driving performance. ADHD subjects were divided into 2 groups according to their level of sleepiness at the MWT: the ”sleepy” group consisted of twenty subjects (mean sleep latency (SL) = 23.9 ± 1.3 min) and the “alert” group included sixteen subjects (LE = 37.3 ± 1 min)( p = 0.001). We observed that more than half of the ADHD subjects exhibit a sleep disorder: 31% among the alert ADHD subjects and 65% among sleepy ADHD subjects. But 35% of the sleepy ADHD subjects did not presented a sleep disorder. About driving performance, there were significant differences between driving performance (observed by the number of lines crossing) between the control group and the alert ADHD group ( p = 0.05) and the control group and the sleepy ADHD group ( p = 0.02). This study supports the hypothesis that there is a sub group of ADHD patients that present pathological sleepiness. In our sample, half of the patients suffer of excessive daytime sleepiness. But it is appropriate to question the origin of the sleepiness found in the other patients and to wonder if they represent a particular phenotype. ADHD impacts on driving performances, but it is not possible today to clearly explain them: attention deficit and/or sleepiness? But it is important to focus on this question to provide therapeutic strategy modulated by the clinic. JP rénéric.