ARTICLE IN PRESS Sleep Medicine ■■ (2015) ■■–■■
Contents lists available at ScienceDirect
Sleep Medicine j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / s l e e p
Original Article
Prevalence and determinants of subjective sleepiness in healthy elderly with unrecognized obstructive sleep apnea Emilia Sforza *, Vincent Pichot, Magali Saint Martin, Jean Claude Barthélémy, Frédéric Roche Faculté de Médecine Jacques Lisfranc, Service de Physiologie Clinique et de l’Exercice (Pole Hospitalier NOL), CHU Nord, Université Jean Monnet, SaintEtienne, SNA EPIS EA 4607, PRES de Lyon, France
A R T I C L E
I N F O
Article history: Received 4 November 2014 Received in revised form 1 March 2015 Accepted 11 March 2015 Available online Keywords: Obstructive sleep apnea Elderly Sleepiness Autonomic nervous system activity
A B S T R A C T
Objective: Obstructive sleep apnea (OSA) is associated with behavioral consequences such as excessive daytime sleepiness (EDS). The aim of this study was to establish the presence of sleepiness in elderly with unrecognized OSA and the factors explaining its occurrence. Methodology: A total of 825 healthy elderly (aged ≥65 years) undergoing clinical, respiratory polygraphy, and heart-rate variability analysis were studied. According to the apnea–hypopnea index (AHI), the subjects were stratified in four categories: no-OSA (AHI AHI 10 s. The absence of rib-cage movement associated with apnea defined the event as central, while a progressive increase in PTT and respiratory efforts allowed for the definition of the event as obstructive. To minimize the potential overestimation of sleep duration, subjects completed a “sleep diary” to mark that start and end points for analysis mirroring lights off and lights on. The AHI was defined as the ratio of the number of apnea or hypopnea episodes per hour of reported sleep time. The indices of nocturnal hypoxemia were as follows: the mean SaO2, the percentage of recorded time with an SaO2 below 90%, the minimum SaO2 value recorded during sleep (minimum SaO2), and the oxygen desaturation index (ODI, ie, number of episodes of oxyhemoglobin desaturation per hour of reported sleep time during which blood oxygen level decreased by 3% or more). According to the severity of OSA, subjects were stratified by different AHI cutoffs, resulting in no OSA (AHI AHI 50 ms (pNN50), and the SDNN index (the mean of the standard deviation of normal RRIs in all 5-min segments) were analyzed for each epoch. SDNN reflects the overall HRV, while RMSSD as well as pNN50 reflect parasympathetic activity. Frequency-domain measurements based on the fast-Fourier transform (FFT) algorithm were applied to the regularly sampled interpolation of the RRI time series. Spectral power was calculated for three frequency bands: very low frequency (VLF: 0–0.04 Hz), low frequency (LF: 0.04–0.15 Hz), high frequency (HF: 0.15–0.4 Hz), and total power (0.00–0.40 Hz). The LF/HF ratio as well as LF and HF values were expressed in normalized percentage units (LFnu = 100 × LF/(Ptot-VLF) and HFnu = 100 × HF/ (Ptot-VLF)). The LF/HF ratio and HF were computed as markers for sympathovagal balance and parasympathetic activity, respectively. 2.3. Statistical analyses Study population characteristics are reported as mean ± SD for continuous variables, and counts and percentages for categorical variables. Analysis of variance (ANOVA) with Bonferroni’s correction for multiple comparisons was used to estimate differences between groups stratified according to AHI. Differences between sleepy and nosleepy subjects both in the total group and only in the OSA group were assessed with the chi-squared test (X2) for categorical variables and t-test for continuous variables. Spearman’s rho correlation coefficients were calculated to assess the relationship between the ESS value and the various clinical and instrumental parameters. Multiple regression analysis was used where the ESS was the dependent variable and polygraphic data were the independent variables with adjustment for potential confounding factors such as gender, BMI, and depression. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 17 for Windows (SPSS Inc., Chicago, IL, USA) and Stata release 11 (Stat Corp, College Station, TX, USA). All reported p-values are two-tailed, with the threshold of statistical significance set at p < 0.05. 3. Results 3.1. Total population Table 1 shows the clinical, anthropometric, and polygraphic data for the total sample and for the four groups of OSA subjects stratified according to AHI.
Table 1 Clinical, anthropometric, and polygraphic data for the total sample and the four groups of subjects stratified according to the apnea–hypopnea index (mean ± SD).
Females (%) BMI (kg/m2) Diabetes (%) Dyslipidemia (%) Hypertension (%) Smoking (%) Anxiety score Depression score ESS Sleepy subjects (%) 24-h SBP (mm Hg) 24-h DBP (mm Hg) AHI (n/h) ODI (n/h) Mean SaO2% (%) Minimal SaO2 (%) Time with SaO2
Contents lists available at ScienceDirect
Sleep Medicine j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / s l e e p
Original Article
Prevalence and determinants of subjective sleepiness in healthy elderly with unrecognized obstructive sleep apnea Emilia Sforza *, Vincent Pichot, Magali Saint Martin, Jean Claude Barthélémy, Frédéric Roche Faculté de Médecine Jacques Lisfranc, Service de Physiologie Clinique et de l’Exercice (Pole Hospitalier NOL), CHU Nord, Université Jean Monnet, SaintEtienne, SNA EPIS EA 4607, PRES de Lyon, France
A R T I C L E
I N F O
Article history: Received 4 November 2014 Received in revised form 1 March 2015 Accepted 11 March 2015 Available online Keywords: Obstructive sleep apnea Elderly Sleepiness Autonomic nervous system activity
A B S T R A C T
Objective: Obstructive sleep apnea (OSA) is associated with behavioral consequences such as excessive daytime sleepiness (EDS). The aim of this study was to establish the presence of sleepiness in elderly with unrecognized OSA and the factors explaining its occurrence. Methodology: A total of 825 healthy elderly (aged ≥65 years) undergoing clinical, respiratory polygraphy, and heart-rate variability analysis were studied. According to the apnea–hypopnea index (AHI), the subjects were stratified in four categories: no-OSA (AHI AHI 10 s. The absence of rib-cage movement associated with apnea defined the event as central, while a progressive increase in PTT and respiratory efforts allowed for the definition of the event as obstructive. To minimize the potential overestimation of sleep duration, subjects completed a “sleep diary” to mark that start and end points for analysis mirroring lights off and lights on. The AHI was defined as the ratio of the number of apnea or hypopnea episodes per hour of reported sleep time. The indices of nocturnal hypoxemia were as follows: the mean SaO2, the percentage of recorded time with an SaO2 below 90%, the minimum SaO2 value recorded during sleep (minimum SaO2), and the oxygen desaturation index (ODI, ie, number of episodes of oxyhemoglobin desaturation per hour of reported sleep time during which blood oxygen level decreased by 3% or more). According to the severity of OSA, subjects were stratified by different AHI cutoffs, resulting in no OSA (AHI AHI 50 ms (pNN50), and the SDNN index (the mean of the standard deviation of normal RRIs in all 5-min segments) were analyzed for each epoch. SDNN reflects the overall HRV, while RMSSD as well as pNN50 reflect parasympathetic activity. Frequency-domain measurements based on the fast-Fourier transform (FFT) algorithm were applied to the regularly sampled interpolation of the RRI time series. Spectral power was calculated for three frequency bands: very low frequency (VLF: 0–0.04 Hz), low frequency (LF: 0.04–0.15 Hz), high frequency (HF: 0.15–0.4 Hz), and total power (0.00–0.40 Hz). The LF/HF ratio as well as LF and HF values were expressed in normalized percentage units (LFnu = 100 × LF/(Ptot-VLF) and HFnu = 100 × HF/ (Ptot-VLF)). The LF/HF ratio and HF were computed as markers for sympathovagal balance and parasympathetic activity, respectively. 2.3. Statistical analyses Study population characteristics are reported as mean ± SD for continuous variables, and counts and percentages for categorical variables. Analysis of variance (ANOVA) with Bonferroni’s correction for multiple comparisons was used to estimate differences between groups stratified according to AHI. Differences between sleepy and nosleepy subjects both in the total group and only in the OSA group were assessed with the chi-squared test (X2) for categorical variables and t-test for continuous variables. Spearman’s rho correlation coefficients were calculated to assess the relationship between the ESS value and the various clinical and instrumental parameters. Multiple regression analysis was used where the ESS was the dependent variable and polygraphic data were the independent variables with adjustment for potential confounding factors such as gender, BMI, and depression. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 17 for Windows (SPSS Inc., Chicago, IL, USA) and Stata release 11 (Stat Corp, College Station, TX, USA). All reported p-values are two-tailed, with the threshold of statistical significance set at p < 0.05. 3. Results 3.1. Total population Table 1 shows the clinical, anthropometric, and polygraphic data for the total sample and for the four groups of OSA subjects stratified according to AHI.
Table 1 Clinical, anthropometric, and polygraphic data for the total sample and the four groups of subjects stratified according to the apnea–hypopnea index (mean ± SD).
Females (%) BMI (kg/m2) Diabetes (%) Dyslipidemia (%) Hypertension (%) Smoking (%) Anxiety score Depression score ESS Sleepy subjects (%) 24-h SBP (mm Hg) 24-h DBP (mm Hg) AHI (n/h) ODI (n/h) Mean SaO2% (%) Minimal SaO2 (%) Time with SaO2
