Obstructive Sleep Apnea Syndrome Phenotyping by Cluster Analysis: Typical Sleepy, Obese Middle-aged Men with Desaturating Events are A Minority of Patients in A Multi-ethnic Cohort of 33% Women.

OBJECTIVE: Several clinical obstructive sleep apnea syndrome (OSAS) phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified. The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients. METHODS: This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period. Demographic, symptom, comorbidity, polysomnographic, and continuous positive airway pressure (CPAP) adherence data were collected. Statistical analyses were performed to identify clusters of patients. RESULTS: A total of 567 patients were included (67% men, 54±13 years, body mass index: 32±7 kg/m2, 65% Caucasian, 32% European African). Five clusters were identified: less severe OSAS (n=172); healthier severe OSAS (n=160); poorly sleeping OSAS patients with cardiometabolic comorbidities (n=87); younger obese men with sleepiness at the wheel (n=94); sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities (n=54). Patients in clusters 3 and 5 were older than those in clusters 2 and 4 (P=0.034). Patients in clusters 4 and 5 were significantly more obese than those in the other clusters (P=0.04). No significant differences were detected in terms of symptoms and comorbidities. Polysomnographic profiles were very discriminating between clusters. CPAP adherence was similar in all clusters but, among adherent patients, daily usage was more important in cluster 1 (less severe patients) than in cluster 5. CONCLUSION: This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33% females.

Cardiometabolic comorbidities in obstructive sleep apnea patients are related to disease severity, nocturnal hypoxemia, and decreased sleep quality.

BACKGROUND: Obstructive sleep apnea syndrome (OSA) is currently recognized as an independent risk factor for hypertension, arrhythmia, coronary heart disease, stroke, and metabolic disorders (e.g. diabetes, dyslipidemia). In clinical practice, apnea-hypopnea index (AHI) is the marker used to classify disease severity and guide treatment. However, AHI alone does not sufficiently identify OSA patients at risk for cardiometabolic comorbidities. With this in mind, the aim of this retrospective study was to determine whether some polysomnographic parameters (e.g. apnea-hypopnea duration, sleep structure, nocturnal hypoxemia) are specifically associated with cardiometabolic comorbidities in OSA. METHODS: In this retrospective study, 1717 patients suffering from moderate/severe OSA were included between 2013 and 2017. Data on demographics, comorbidities, and polysomnographic characteristics were collected and analyzed to identify factors associated with cardiometabolic complications. RESULTS: The medical files of 1717 patients (68% male) were reviewed. The mean AHI was 43.1 +/- 27.7 with 57.3% of patients suffering from severe OSA, and 52% from at least one cardiovascular comorbidity (CVCo). Diabetes affected 22% of the patients and 27% exhibited dyslipidemia. Patients affected by CVCos were older, and more often women and non-smokers. These patients also had worse sleep quality, and a more marked intermittent/global nocturnal hypoxemia. With regard to diabetes, diabetics were older, more often non-smoker, non-drinker women, and were more obese. These patients also exhibited more severe OSA, especially in non-REM (NREM) sleep, worse sleep quality, and a more marked intermittent/global nocturnal hypoxemia. Dyslipidemia was more frequent in the absence of alcohol consumption, and was associated with OSA severity, decreased sleep quality, and longer AH in REM sleep. CONCLUSIONS: This study identifies demographic and polysomnographic factors associated with cardiometabolic comorbidities. Patients (especially women) suffering from more severe OSA, longer sleep apneas and hypopneas, worse sleep quality, and marked intermittent/global nocturnal hypoxemia are more likely to develop cardiometabolic comorbidities. This should stimulate clinicians to obtain adequate treatment in this population.

African ethnicity is associated with a higher prevalence of diabetes in obstructive sleep apnea patients: results of a retrospective analysis.

PURPOSE: Obstructive sleep apnea (OSA) syndrome is a well-recognized independent risk factor for cardiovascular disease and its prevalence is increasing. OSA symptomology, polysomnographic features, and comorbidities are heterogeneous among patients. Ethnicity is thought to influence OSA phenotypes, but extensive knowledge of OSA ethnic patterns is lacking. The primary aim of the present study was to compare comorbidities in Caucasian and African OSA. Secondary aims were to observe OSA symptomatology, polysomnographic characteristics, and CPAP adherence in these two ethnic groups. METHODS: In this retrospective study, 1717 patients suffering from moderate/severe OSA were included between 2013 and 2017. Data on demographics, symptomatology, comorbidities, polysomnographic characteristics, and CPAP adherence were collected. Data were analyzed to identify potential differences between Caucasians and Africans. RESULTS: Despite healthier lifestyles and lower BMI, a higher prevalence of diabetes but less cardiac comorbidities and dyslipidemia was observed in the African population. Younger African patients (< 56 years) suffered more from cognitive impairment than Caucasians and both younger and older Africans complained more of nighttime choking than Caucasians. In analysis of polysomnographic data, Africans had higher apnea-hypopnea index (AHI) in REM sleep, lower supine AHI, lower desaturation time, and lower periodic leg movements index. CONCLUSIONS: Compared with Caucasians, African OSA showed a particular comorbidity profile. There are younger patients who exhibit more diabetes but less cardiac comorbidities than the Caucasians. African diabetics should be more promptly referred for OSA testing. Moreover, as they suffer more often from choking and cognitive impairment, OSA treatment could positively impact their quality of life.

The validity of two commercially-available sleep trackers and actigraphy for assessment of sleep parameters in obstructive sleep apnea patients.

OBJECTIVE: The use of activity and sleep trackers that operate through dedicated smartphone applications has become popular in the general population. However, the validity of the data they provide has been disappointing and only Total Sleep Time (TST) is reliably recorded in healthy individuals for any of the devices tested. The purpose of this study was to evaluate the ability of two sleep trackers (Withings pulse 02 (W) and Jawbone Up (U)) to measure sleep parameters in patients suffering from obstructive sleep apnea (OSA). METHODS: All patients evaluated for OSA in our sleep laboratory underwent overnight polysomnography (PSG). PSG was conducted simultaneously with three other devices: two consumer-level sleep monitors (U and W) and one actigraph (Bodymedia SenseWear Pro Armband (SWA)). RESULTS: Of 36 patients evaluated, 22 (17 men) were diagnosed with OSA (mean apnea-hypopnea index of 37+ 23/h). Single comparisons of sleep trackers (U and W) and actigraph (SWA) were performed. Compared to PSG, SWA correctly assessed TST and Wake After Sleep Onset (WASO), and U and W correctly assessed Time In Bed (TIB) and light sleep. Intraclass correlations (ICC) revealed poor validity for all parameters and devices, except for WASO assessed by SWA. CONCLUSIONS: This is the first study assessing the validity of sleep trackers in OSA patients. In this series, we have confirmed the limited performance of wearable sleep monitors that has been previously observed in healthy subjects. In OSA patients, wearable app-based health technologies provide a good estimation of TIB and light sleep but with very poor ICC.

Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals.

INTRODUCTION: Wearable health devices have become trendy among consumers, but it is not known whether they accurately measure sleep and physical activity parameters. To address this question, we have studied the measured data of two consumer-level activity monitors (Up Move Jawbone® (U) and Withings Pulse 02® (W)) and compared it with reference methods for sleep and activity recordings, namely the Bodymedia SenseWear Pro Armband® actigraph (SWA) and home-polysomnography (H-PSG). METHODS: Twenty healthy patients were assessed at home, during sleep, with the four devices. An additional 24-h period of recording was then planned during which they wore the 2 trackers and the SWA. Physical activity and sleep parameters obtained with the 4 devices were analyzed. RESULTS: Significant correlations with H-PSG were obtained for total sleep time (TST) for all the devices: r=0.48 for W (p=0.04), r=0.63 for U (p=0.002), r=0.7 for SWA (p=0.0003). The best coefficient was obtained with SWA. Significant correlations were also obtained for time in bed (TIB) for U and SWA vs PSG (r=0.79 and r=0.76, p<0.0001 for both) but not for W (r=0.45, p=0.07). No significant correlations were obtained for deep sleep, light sleep, and sleep efficiency (SE) measurements with W, U and SWA. Sleep latency (SL) correlated with H-PSG only when measured against SWA (r=0.5, p=0.02). Physical activity assessment revealed significant correlations for U and W with SWA for step count (both r=0.95 and p<0.0001) and active energy expenditure (EE) (r=0.65 and 0.54; p=0.0006 and p<0.0001). Total EE was also correctly estimated (r=0.75 and 0.52; p<0.0001 and p=0.001). CONCLUSION: Sleep and activity monitors are only able to produce a limited set of reliable measurements, such as TST, step count, and active EE, with a preference for U which performs globally better. Despite the manual activation to sleep mode, U and W were not suitable for giving correct data such as sleep architecture, SE, and SL. In the future, to enhance accuracy of such monitors, researchers and providers have to collaborate to write algorithms based reliably on sleep physiology. It could avoid misleading the consumer.