@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 {1043, title = {Fasting glycemia in sleep disordered breathing: lowering the threshold on oxyhemoglobin desaturation.}, journal = {Sleep}, volume = {31}, year = {2008}, month = {2008 Jul}, pages = {1018-24}, abstract = {

STUDY OBJECTIVES: Commonly used definitions of sleep-disordered breathing (SDB) are based on identifying discrete events of breathing abnormalities during sleep that are accompanied by an oxyhemoglobin desaturation (delta SaO2) of at least 4\%. However, it is not known whether disordered breathing events with oxyhemoglobin desaturation less than 4\% are associated with clinical sequelae such as abnormalities in fasting glycemia.

DESIGN: Cross-sectional study.

SUBJECTS AND SETTING: Participants from the Sleep Heart Health Study (SHHS) with a fasting glucose measurement made within a year of the baseline polysomnogram.

MEASUREMENTS AND RESULTS: SDB severity was defined using the apnea-hypopnea index (AHI) and the hypopnea index (HI) by counting events with different levels of oxyhemoglobin desaturation (0.0\%-1.9\%, 2.0\%-2.9\%, 3.0\%-3.9\%, > or = 4.0\%). Fasting glucose levels were used to classify individuals into normal (<100 mg/dL), impaired (100-125 mg/dL), and diabetic (> or = 126 mg/dL) groups. Ordinal logistic regression was used to determine the adjusted relative odds of an abnormal glucose value across quartiles of the hypopnea index, independent of factors such as age, body mass index, waist circumference, and usual sleep duration. The prevalence of impaired and diabetic fasting glucose in the analytical sample was 32.9\% and 5.8\%, respectively. The covariate-adjusted relative odds of impaired or diabetic fasting glucose in the highest versus the lowest AHI quartile was 1.35 (95\% CI: 1.04-1.76) for events with a delta SaO2 > or = 4.0\%, 1.72 (95\% CI: 1.20-2.48) for events with a delta SaO2 between 3.0\%-3.9\%, 1.41 (95\% CI: 1.07-1.86) for events with a delta SaO2 between 2.0\%-2.9\%, and 1.07 (95\% CI: 0.84-1.37) for events with a delta SaO2 between 0.0\%-1.9\%. The corresponding odds ratios for the HI were 1.47 (95\% CI: 1.13-1.92), 2.25 (95\% CI: 1.59-3.19), 1.44 (95\% CI: 1.09-1.90), and 1.15 (95\% CI: 0.90-1.47), respectively.

CONCLUSIONS: The results of this study indicate that SDB events accompanied by oxyhemoglobin desaturation of between 2\% to 4\% are associated with fasting hyperglycemia. These findings suggest that milder degrees of SDB may predispose to adverse metabolic outcomes.

}, keywords = {Aged, Blood Glucose, Body Mass Index, Cohort Studies, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Fasting, Female, Humans, Male, Middle Aged, Odds Ratio, Oxygen, Oxyhemoglobins, Polysomnography, Reference Values, Sleep Apnea, Obstructive}, issn = {0161-8105}, author = {Stamatakis, Katherine and Sanders, Mark H and Caffo, Brian and Resnick, Helaine E and Gottlieb, Dan J and Mehra, Reena 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} } @article {1253, title = {Utility of sleep stage transitions in assessing sleep continuity.}, journal = {Sleep}, volume = {33}, year = {2010}, month = {2010 Dec}, pages = {1681-6}, abstract = {

STUDY OBJECTIVES: Sleep continuity is commonly assessed with polysomnographic measures such as sleep efficiency, sleep stage percentages, and the arousal index. The aim of this study was to examine whether the transition rate between different sleep stages could be used as an index of sleep continuity to predict self-reported sleep quality independent of other commonly used metrics.

DESIGN AND SETTING: Analysis of the Sleep Heart Health Study polysomnographic data.

PARTICIPANTS: A community cohort.

MEASUREMENTS AND RESULTS: Sleep recordings on 5,684 participants were deemed to be of sufficient quality to allow visual scoring of NREM and REM sleep. For each participant, we tabulated the frequency of transitions between wake, NREM sleep, and REM sleep. An overall transition rate was determined as the number of all transitions per hour sleep. Stage-specific transition rates between wake, NREM sleep, and REM sleep were also determined. A 5-point Likert scale was used to assess the subjective experience of restless and light sleep the morning after the sleep study. Multivariable regression models showed that a high overall sleep stage transition rate was associated with restless and light sleep independent of several covariates including total sleep time, percentages of sleep stages, wake time after sleep onset, and the arousal index. Compared to the lowest quartile of the overall transition rate (<7.76 events/h), the odds ratios for restless sleep were 1.27, 1.42, and 1.38, for the second (7.77-10.10 events/h), third (10.11-13.34 events/h), and fourth (>=13.35 events/h) quartiles, respectively. Analysis of stage-specific transition rates showed that transitions between wake and NREM sleep were also independently associated with restless and light sleep.

CONCLUSIONS: Assessing overall and stage-specific transition rates provides a complementary approach for assessing sleep continuity. Incorporating such measures, along with conventional metrics, could yield useful insights into the significance of sleep continuity for clinical outcomes.

}, keywords = {Age Factors, Aged, Arousal, Body Mass Index, Cohort Studies, Female, Humans, Male, Middle Aged, Polysomnography, Predictive Value of Tests, Risk Factors, Sex Factors, Sleep Stages, Sleep Wake Disorders}, issn = {0161-8105}, author = {Laffan, Alison and Caffo, Brian and Swihart, Bruce J and Punjabi, Naresh M} } @article {1540, title = {Sleep-disordered breathing and caffeine consumption: results of a community-based study.}, journal = {Chest}, volume = {142}, year = {2012}, month = {2012 Sep}, pages = {631-638}, abstract = {

BACKGROUND: Sleepiness is one of the most burdensome symptoms of sleep-disordered breathing (SDB). While caffeine is frequently used to avert sleepiness, the association between SDB and caffeine use has not been thoroughly explored. The current study examined whether SDB is associated with caffeine consumption and if factors such as sex, age, and daytime sleepiness explain or modify the association.

METHODS: Data from the Sleep Heart Health Study, a community-based study on the consequences of SDB, were used to characterize the association between SDB and caffeine intake. SDB was assessed with full-montage polysomnography. Caffeine use was quantified as the number of cans of soda or the cups of coffee or tea consumed daily. The Epworth Sleepiness Scale was used to assess daytime sleepiness. Multivariable negative binomial regression models were used to characterize the independent association between SDB and caffeine use.

RESULTS: Caffeinated soda, but not tea or coffee, intake was independently associated with SDB severity. Compared with participants without SDB, the relative ratios for caffeinated soda consumption in women with mild, moderate, and severe SDB were 1.20 (CI, 1.03-1.41), 1.46 (CI, 1.14-1.87), and 1.73 (CI, 1.23-2.42), respectively. For men, an association was only noted with severe SDB and caffeinated soda use. Age did not modify the SDB-caffeine association, and sleepiness could not explain the observed associations.

CONCLUSIONS: SDB is independently associated with caffeinated soda use in the general community. Identifying excessive caffeine used in SDB has potential significance given the cardiovascular effects of caffeine and untreated SDB.

}, keywords = {Age Factors, Aged, Caffeine, Carbonated Beverages, Coffee, Data Collection, Drinking Behavior, Female, Humans, Male, Middle Aged, Polysomnography, Prevalence, Retrospective Studies, Sex Factors, Sleep Apnea Syndromes, Tea}, issn = {1931-3543}, doi = {10.1378/chest.11-2894}, author = {Aurora, R Nisha and Crainiceanu, Ciprian and Caffo, Brian and Punjabi, Naresh M} }