@article {715, title = {Sleep and sleep-disordered breathing in adults with predominantly mild obstructive airway disease.}, journal = {Am J Respir Crit Care Med}, volume = {167}, year = {2003}, month = {2003 Jan 01}, pages = {7-14}, abstract = {

Neither the association between obstructive airways disease (OAD) and sleep apnea-hypopnea (SAH) nor the sleep consequences of each disorder alone and together have been characterized in an adult community setting. Our primary aims were (1) to determine if there is an association between OAD and SAH and (2) identify predictors of oxyhemoglobin desaturation during sleep in persons having OAD with and without SAH. Polysomnography and spirometry results from 5,954 participants in the Sleep Heart Health Study were analyzed. OAD was defined by a FEV1/FVC value less than 70\%. Assessment of SAH prevalence in OAD was performed using thresholds of respiratory disturbance index (RDI) greater than 10 and greater than 15. A total of 1,132 participants had OAD that was predominantly mild (FEV1/FVC 63.81 +/- 6.56\%, mean +/- SD). SAH was not more prevalent in participants with OAD than in those without OAD (22.32 versus 28.86\%, with and without OAD, respectively, at RDI threshold values greater than 10; and 13.97 versus 18.63\%, with and without OAD, respectively, at RDI threshold value greater than 15). In the absence of SAH, the adjusted odds ratio for sleep desaturation (> 5\% total sleep time with saturation < 90\%) was greater than 1.9 when FEV1/FVC was less than 65\%. Participants with both OAD and SAH had greater sleep perturbation and desaturation than those with one disorder. Generally mild OAD alone was associated with minimally altered sleep quality. We conclude that (1) there is no association between generally mild OAD and SAH; (2) exclusive of SAH and after adjusting for demographic factors and awake oxyhemoglobin saturation, an FEV1/FVC value less than 65\% is associated with increased risk of sleep desaturation; (3) desaturation is greater in persons with both OAD and SAH compared with each of these alone; and (4) individuals with generally mild OAD and without SAH in the community have minimally perturbed sleep.

}, keywords = {Aged, Cohort Studies, Female, Forced Expiratory Volume, Humans, Male, Middle Aged, Oxyhemoglobins, Polysomnography, Prospective Studies, Pulmonary Disease, Chronic Obstructive, Sleep, Sleep Apnea, Obstructive, Spirometry, Vital Capacity}, issn = {1073-449X}, doi = {10.1164/rccm.2203046}, author = {Sanders, Mark H and Newman, Anne B and Haggerty, Catherine L and Redline, Susan and Lebowitz, Michael and Samet, Jonathan and O{\textquoteright}Connor, George T and Punjabi, Naresh M and Shahar, Eyal} } @article {904, title = {Cigarette smoking and nocturnal sleep architecture.}, journal = {Am J Epidemiol}, volume = {164}, year = {2006}, month = {2006 Sep 15}, pages = {529-37}, abstract = {

Cigarette smoking has been associated with a high prevalence of sleep-related complaints. However, its effects on sleep architecture have not been fully examined. The primary objective of this investigation was to assess the impact of cigarette smoking on sleep architecture. Polysomnography was used to characterize sleep architecture among 6,400 participants of the Sleep Heart Health Study (United States, 1994-1999). Sleep parameters included total sleep time, latency to sleep onset, sleep efficiency, and percentage of time in each sleep stage. The study sample consisted of 2,916 never smokers, 2,705 former smokers, and 779 current smokers. Compared with never smokers, current smokers had a longer initial sleep latency (5.4 minutes, 95\% confidence interval (CI): 2.9, 7.9) and less total sleep time (14.0 minutes, 95\% CI: 6.4, 21.7). Furthermore, relative to never smokers, current smokers also had more stage 1 sleep (relative proportion = 1.24, 95\% CI: 1.14, 1.33) and less slow wave sleep (relative proportion = 0.86, 95\% CI: 0.78, 0.95). Finally, no differences in sleep architecture were noted between former and never smokers. The results of this study show that cigarette smoking is independently associated with disturbances in sleep architecture, including a longer latency to sleep onset and a shift toward lighter stages of sleep. Nicotine in cigarette smoke and acute withdrawal from it may contribute to disturbances in sleep architecture.

}, keywords = {Aged, Arousal, Chi-Square Distribution, Female, Humans, Male, Middle Aged, Polysomnography, Prevalence, Regression Analysis, Risk Factors, Sleep Stages, Sleep Wake Disorders, Smoking, United States}, issn = {0002-9262}, doi = {10.1093/aje/kwj231}, author = {Zhang, Lin and Samet, Jonathan and Caffo, Brian and Punjabi, Naresh M} } @article {993, title = {Power spectral analysis of EEG activity during sleep in cigarette smokers.}, journal = {Chest}, volume = {133}, year = {2008}, month = {2008 Feb}, pages = {427-32}, abstract = {

BACKGROUND: Research on the effects of cigarette smoking on sleep architecture is limited. The objective of this investigation was to examine differences in sleep EEG between smokers and nonsmokers.

METHODS: Smokers and nonsmokers who were free of all medical comorbidities were matched on different factors, including age, gender, race, body mass index, and anthropometric measures. Home polysomnography was conducted using a standard recording montage. Sleep architecture was assessed using visual sleep-stage scoring. The discrete fast Fourier transform was used to calculate the EEG power spectrum for the entire night within contiguous 30-s epochs of sleep for the following frequency bandwidths: delta (0.8 to 4.0 Hz); theta (4.1 to 8.0 Hz); alpha (8.1 to 13.0 Hz); and beta (13.1 to 20.0 Hz).

RESULTS: Conventional sleep stages were similar between the two groups. However, spectral analysis of the sleep EEG showed that, compared to nonsmokers, smokers had a lower percentage of EEG power in the delta-bandwidth (59.7\% vs 62.6\%, respectively; p < 0.04) and higher percentage of EEG power in alpha-bandwidth (15.6\% vs 12.5\%, respectively; p < 0.001). Differences in the EEG power spectrum between smokers and nonsmokers were greatest in the early part of the sleep period and decreased toward the end. Subjective complaints of lack of restful sleep were also more prevalent in smokers than in nonsmokers (22.5\% vs 5.0\%, respectively; p < 0.02) and were explained, in part, by the differences in EEG spectral power.

CONCLUSIONS: Cigarette smokers manifest disturbances in the sleep EEG that are not evident in conventional measures of sleep architecture. Nicotine in cigarette smoke and withdrawal from it during sleep may contribute to these changes and the subjective experience of nonrestorative sleep.

}, keywords = {Electroencephalography, Female, Humans, Logistic Models, Male, Middle Aged, Polysomnography, Sleep, Smoking, Spectroscopy, Fourier Transform Infrared}, issn = {0012-3692}, doi = {10.1378/chest.07-1190}, author = {Zhang, Lin and Samet, Jonathan and Caffo, Brian and Bankman, Isaac and Punjabi, Naresh M} } @article {1083, title = {Prospective study of sleep-disordered breathing and hypertension: the Sleep Heart Health Study.}, journal = {Am J Respir Crit Care Med}, volume = {179}, year = {2009}, month = {2009 Jun 15}, pages = {1159-64}, abstract = {

RATIONALE: Cross-sectional epidemiologic studies show an association between sleep-disordered breathing and hypertension, but only one cohort study has examined sleep-disordered breathing as a risk factor for incident hypertension.

OBJECTIVES: To examine whether sleep-disordered breathing increases the risk of incident hypertension among persons 40 years of age and older.

METHODS: In a prospective cohort study, we analyzed data from 2,470 participants who at baseline did not have hypertension, defined as blood pressure of at least 140/90 mm Hg or taking antihypertensive medication. The apnea-hypopnea index (AHI), the number of apneas plus hypopneas per hour of sleep, was measured by overnight in-home polysomnography. We estimated odds ratios for developing hypertension during 5 years of follow-up according to baseline AHI.

MEASUREMENTS AND MAIN RESULTS: The odds ratios for incident hypertension increased with increasing baseline AHI; however, this relationship was attenuated and not statistically significant after adjustment for baseline body-mass index. Although not statistically significant, the observed association between a baseline AHI greater than 30 and future hypertension (odds ratio, 1.51; 95\% confidence interval, 0.93-2.47) does not exclude the possibility of a modest association.

CONCLUSIONS: Among middle-aged and older persons without hypertension, much of the relationship between AHI and risk of incident hypertension was accounted for by obesity. After adjustment for body mass index, the AHI was not a significant predictor of future hypertension, although a modest influence of an AHI greater than 30 on hypertension could not be excluded.

}, keywords = {Adult, Blood Pressure, Confidence Intervals, Cross-Sectional Studies, Female, Follow-Up Studies, Humans, Hypertension, Incidence, Male, Middle Aged, Odds Ratio, Polysomnography, Prognosis, Prospective Studies, Risk Factors, Sleep Apnea Syndromes, Time Factors, United States}, issn = {1535-4970}, doi = {10.1164/rccm.200712-1809OC}, author = {O{\textquoteright}Connor, George T and Caffo, Brian and Newman, Anne B and Quan, Stuart F and Rapoport, David M and Redline, Susan and Resnick, Helaine E and Samet, Jonathan and Shahar, Eyal} } @article {1252, title = {A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the Sleep Heart Health Study.}, journal = {Sleep}, volume = {33}, year = {2010}, month = {2010 Dec}, pages = {1641-8}, abstract = {

This manuscript considers a data-mining approach for the prediction of mild obstructive sleep disordered breathing, defined as an elevated respiratory disturbance index (RDI), in 5,530 participants in a community-based study, the Sleep Heart Health Study. The prediction algorithm was built using modern ensemble learning algorithms, boosting in specific, which allowed for assessing potential high-dimensional interactions between predictor variables or classifiers. To evaluate the performance of the algorithm, the data were split into training and validation sets for varying thresholds for predicting the probability of a high RDI (>=7 events per hour in the given results). Based on a moderate classification threshold from the boosting algorithm, the estimated post-test odds of a high RDI were 2.20 times higher than the pre-test odds given a positive test, while the corresponding post-test odds were decreased by 52\% given a negative test (sensitivity and specificity of 0.66 and 0.70, respectively). In rank order, the following variables had the largest impact on prediction performance: neck circumference, body mass index, age, snoring frequency, waist circumference, and snoring loudness.

}, keywords = {Age Factors, Aged, Algorithms, Body Mass Index, Cohort Studies, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Risk Factors, ROC Curve, Sleep Apnea Syndromes, Snoring, Waist Circumference}, issn = {0161-8105}, author = {Caffo, Brian and Diener-West, Marie and Punjabi, Naresh M and Samet, Jonathan} }