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Longitudinal Study of Moderate Weight Change and Sleep-Disordered Breathing
Paul E. Peppard, PhD;
Terry Young, PhD;
Mari Palta, PhD;
Jerome Dempsey, PhD;
James Skatrud, MD
JAMA. 2000;284:3015-3021.
ABSTRACT
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Context Excess body weight is positively associated with sleep-disordered breathing (SDB), a prevalent condition in the US general population. No large study has been conducted of the longitudinal association between SDB and change in weight.
Objective To measure the independent longitudinal association between weight change and change in SDB severity.
Design Population-based, prospective cohort study conducted from July 1989 to January 2000.
Setting and Participants Six hundred ninety randomly selected employed Wisconsin residents (mean age at baseline, 46 years; 56% male) who were evaluated twice at 4-year intervals for SDB.
Main Outcome Measures Percentage change in the apnea-hypopnea index (AHI; apnea events + hypopnea events per hour of sleep) and odds of developing moderate-to-severe SDB (defined by an AHI  15 events per hour of sleep), with respect to change in weight.
Results Relative to stable weight, a 10% weight gain predicted an approximate 32% (95% confidence interval [CI], 20%-45%) increase in the AHI. A 10% weight loss predicted a 26% (95% CI, 18%-34%) decrease in the AHI. A 10% increase in weight predicted a 6-fold (95% CI, 2.2-17.0) increase in the odds of developing moderate-to-severe SDB.
Conclusions Our data indicate that clinical and public health programs that result in even modest weight control are likely to be effective in managing SDB and reducing new occurrence of SDB.
INTRODUCTION
Sleep-disordered breathing (SDB), a condition characterized by repeated episodes of apnea and hypopnea events during sleep, is highly prevalent among adults in the United States and other Western countries.1-6 The high prevalence has raised concerns of the public health burden of SDB because of demonstrated cross-sectional and retrospective associations between SDB and behavioral7-15 and cardiovascular16-23 morbidity. Recently, indicators of even mild SDB have been significantly related to hypertension, cardiovascular disease, and mortality in population-based prospective studies.24-27 Although nightly use of continuous positive airway pressure can prevent apnea and hypopnea events, this therapy poses too high a life-long patient burden to be practical for mild or asymptomatic SDB. Thus, risk-factor modification may be the most feasible way to reduce the prevalence of SDB on a large scale.
Obesity, a strong correlate of SDB, is extremely prevalent in the United States and is increasing to epidemic proportions in the general population.28-30 Obesity has been hypothesized to alter breathing during sleep via multiple mechanisms, including alteration of upper airway structure and function and disturbance of the relation between respiratory drive and load compensation.31 If obesity is causally related to SDB, weight loss and the prevention of weight gain may offer the best hope for reducing the occurrence and severity of SDB and its related morbidity. Consequently, there is a pressing need to quantify the effect of weight change on SDB. Most previous studies linking obesity and SDB have used cross-sectional convenience samples of patients from sleep-disorders clinics32-40 or cross-sectional population-based samples.3-6,22, 41-44 Several small studies, most lacking control groups, have found marked reductions in indicators of SDB following surgical45-49 or diet-related weight loss49-58 in obese patients. There is, however, a paucity of research relating weight gain to SDB incidence and progression, and little is known about the role of weight change in SDB across the spectrum of mild-to-severe SDB.
To date, there has been no large population-based study of the longitudinal association of change in weight and SDB. Longitudinal information is especially crucial in preclinical, asymptomatic people with mild-to-moderate SDB who are most likely to benefit from noninvasive and preventive weight control strategies. Our longitudinal study was designed to measure the degree to which weight gain is associated with increased SDB severity and weight loss with decreased SDB severity. This study uses a sample of participants from the Wisconsin Sleep Cohort Study (WSCS), a continuing prospective study of the natural history of SDB in middle-aged adults.4
METHODS
Participants
Participants in the WSCS are continuously recruited from a stratified random sample of adult men and women employed in a diverse set of job classifications at 5 State of Wisconsin agencies. A detailed description of the sample construction has been previously published.4 Participants completed a baseline overnight protocol that included nocturnal polysomnography and other tests. Approximately 4 years later, baseline participants were invited for follow-up studies.
Criteria precluding WSCS participation included pregnancy, unstable or decompensated cardiopulmonary disease, airway cancers, and recent upper respiratory tract surgery. In addition, for this report, participants were excluded if, at baseline or follow-up, they had sleep studies with unusable physiologic parameters or less than 4 hours of sleep time (n = 42), medical treatment for SDB (n = 20), or physician-diagnosed stroke or cardiovascular disease (n = 56). Finally, participants who experienced weight change in excess of 20% of baseline body weight (n = 28) were excluded from the analyses.
As of January 2000, there were 948 eligible participants with a completed baseline study who were invited for a 4-year follow-up study. Of these, 690 completed a follow-up study (a 72.8% follow-up rate), 242 declined (25.5%), and 16 could not be contacted (undelivered mail or died, 1.7%). Table 1 provides baseline and follow-up key descriptive statistics for all eligible persons invited to participate in the follow-up study and for participants actually used for this analysis.
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Table 1. Summary of Key Variables for Eligible Baseline Participants Invited for a Follow-up Study and Participants in the Follow-up Study*
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Data Collection
Protocols and informed consent documents for WSCS were approved by the institutional review board of the University of Wisconsin Medical School, Madison. Baseline and 4-year follow-up overnight protocols were conducted at the University of Wisconsin General Clinical Research Center using rooms designed to mimic the decor of typical bedrooms. Participants arrived for overnight studies in the early evening. Sleep technicians obtained written informed consent, administered health history and lifestyle questionnaires, and measured blood pressure and body habitus parameters.
Body habitus measures, including height and weight without shoes; waist, neck, and hip girths; and biceps, triceps, subscapular, and suprailiac skinfolds, were measured using standard procedures.59 Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Information on medical history, smoking, alcohol use, education, age, and other sociodemographic factors was obtained by interview and questionnaire.
Following body habitus assessment, technicians affixed polysomnography leads to participants and performed calibrations. An 18-channel polysomnography recording system (Polygraph model 78; Grass Instruments, Quincy, Mass) assessed sleep state, respiratory, and cardiac parameters. Sleep state parameters were determined by electroencephalography, electro-oculography, and chin electromyography. These leads were used to score sleep stage for each 30-second epoch of the polysomnographic record, using conventional criteria.60 Measurement of arterial oxyhemoglobin saturation, oral and nasal airflow, nasal air pressure, and thoracic cage and abdominal respiratory motion were used to assess SDB events. Oxyhemoglobin saturation was recorded continuously using pulse oximetry (Ohmeda 3740, Englewood, Colo). Stalk-mounted thermocouples (ProTec, Hendersonville, Tenn) were used to detect oral and nasal airflow. A pressure transducer (Validyne Engineering Corp, Northridge, Calif) continuously measured air pressure at the nares via nasal prongs. Respiratory inductance plethysmography (Respitrace; Ambulatory Monitoring, Ardsley, NY) continuously recorded thoracic cage and abdominal excursions. Sleep state and respiratory event scorings were performed by trained sleep technicians and reviewed by an expert polysomnographer.
Each 30-second epoch of the polysomnographic records was visually inspected and scored for abnormal breathing events. Cessation of airflow lasting 10 or more seconds defined an apnea event. A discernible reduction in the sum of thoracic cage plus abdomen respiratory inductance plethysmography amplitude associated with a 4% or greater reduction in oxyhemoglobin saturation defined a hypopnea event. The mean number of apnea events plus hypopnea events per hour of objectively measured sleep defined the apnea-hypopnea index (AHI), our summary parameter of SDB.
Statistical Analysis
Descriptive and regression analyses were performed with SAS software, releases 6.12 and 8.00 (SAS Institute Inc, Cary, NC). Two types of models were used to measure the relation between weight change and change in SDB severity. Both approaches are detailed below.
Multiple linear regression models were used to assess the association between change in the AHI and weight change while controlling for potential confounding variables. These models were implemented by regressing the log of the ratio of follow-up AHI divided by baseline AHI (ie, loge[AHI2 + 1/AHI1 + 1], the dependent variable) on the log of the ratio of follow-up weight divided by baseline weight (ie, loge[weight2/weight1], the primary independent variable). Loge([AHI2 + 1]/[AHI1 + 1]), as opposed to other measures of change in the AHI, followed an approximately normal distribution in the WSCS population. The resulting coefficient of loge(weight2/weight1) can be interpreted as approximately the predicted percentage change in AHI related to a 1% weight change. The addition of the constant (1) to both the baseline and follow-up AHI measures was necessary because some participants had an AHI equal to zero. We refer to this model as the "progression" model, although reductions as well as increases in AHI values may be predicted.
Conditional (intrasubject) logistic regression modeling was used to estimate the increased likelihood of developing moderate-to-severe SDB (defined as AHI 15 events/h) associated with percentage weight change. We refer to this as the "incidence" model. Crossing the 15 events/h cutoff in either direction is accommodated by the model, allowing the model to account for an association of both weight gain and loss with changing SDB classification. The conditional model implicitly controls for fixed intraperson characteristics, such as sex and genetic profile.
The following were investigated as interacting and confounding factors in linear regression models, and, when appropriate, in the conditional logistic regression models: sex; baseline values of age, smoking habits (never, ever, and current-use status and cigarette packs per week), alcohol use (usual weekly consumption and amount consumed 24 hours prior to sleep study), menopausal status, body habitus (BMI; weight, height, and skinfold measurements; neck, hip, and waist girths; and waist-to-hip girth ratio), levels of education and physical activity; and 4-year change in smoking habits, alcohol use, menopausal status, and body habitus. Covariates, which substantially altered (>10% change) the regression coefficient for loge(weight2/weight1) in the progression model or the coefficient for percentage weight change in the incidence model, were retained in final models. Interactions between the covariates and weight change were tested for statistical significance. The statistical significance (2-tailed P<.05 for main effects and P<.01 for interactions) of linear regression coefficients was assessed by t tests. Conditional logistic regression coefficients were tested using the Wald  2 statistic.61 Regression diagnostics were performed to assess model fit and adequacy of compliance with modeling assumptions.
Intrasubject variability and measurement error in the AHI prevented meaningful assessment of whether the association of weight change and change in the AHI varied according to the baseline level of AHI. To address this problem, a supplemental analysis was performed using data from 215 participants who had completed baseline, 4-year, and 8-year follow-up sleep laboratory studies. Here, baseline and 4-year follow-up studies were averaged to produce a new "baseline" measured with less error than the AHI based on a single assessment. Using this new baseline AHI variable, we found no evidence for an interaction between baseline AHI and weight change (P>.50 for interaction term). That is, the relation between percentage weight change and percentage AHI change appears to be independent of baseline AHI. Thus, we expect that the regression model results presented here are valid across the range of baseline AHI values analyzed in this study.
RESULTS
At baseline, unadjusted means (SDs) of AHI were 7.4 (13.1) events/h in obese participants (BMI30 kg/m2, n = 268), 2.6 (4.5) events/h in overweight participants (25 BMI<30 kg/m2, n = 241), and 1.2 (2.4) events/h in normal weight participants (BMI<25 kg/m2, n = 181). During 4 years of follow-up, study participants gained a mean (SD) of 2.4 (5.7) kg. The mean (SD) change in AHI was + 1.4 (8.7) events/h. Change in AHI, unadjusted for covariates, was related in a dose-response fashion to change in weight (Figure 1). Of 644 participants who did not have moderate-to-severe SDB at baseline (AHI1<15 events/h), 39 did have moderate-to-severe SDB (AHI215 events/h) at follow-up. These participants experienced a mean 3.9 (6.8) kg weight increase. Of 46 participants with moderate-to-severe SDB at baseline, 17 fell below 15 events/h at follow-up and experienced a mean 3.1 (6.2) kg weight loss. Forty-three participants experienced no change in the AHI (both AHI1 and AHI2 = 0). These participants experienced a mean 2.2 (4.9) kg increase in weight, compared with a mean weight increase of 4.0 (6.9) kg in participants who experienced any increase in the AHI from baseline to follow-up.
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Figure. Mean Change in the Apnea-Hypopnea Index AHI by Weight Change Category
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The SDB progression model is summarized in Table 2. Adjusting for sex, baseline age and BMI, and change in smoking habits, weight change was positively related to change in the AHI. For small weight increments or decrements, each percentage change in weight was associated with an approximate mean 3% change in the AHI. For example, a person who experiences a 10% weight gain is expected to have an approximate 32% increase in AHI beyond the AHI increase that would be expected to occur if weight remained stable. Weight loss was associated with analogous predicted reductions in the AHI.
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Table 2. Estimated Percent Change in the AHI for Selected Decrements and Increments of Percent Body Weight*
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Regression estimates were not materially altered by adjustment for menopausal status, physical activity, alcohol use, or education level, and these variables were not retained in the final progression model. Change in cigarette packs smoked per week did not materially change the association between weight change and AHI change. However, change in smoking habits was retained in final models because smoking cessation was associated with weight gain in this study, and smoking was positively related to increased SDB severity in a previous cross-sectional analysis from the WSCS.62 Baseline values and changes in skinfold thicknesses; neck, waist, and hip girths; and waist-to-hip girth ratio were not significant predictors of change in the AHI independent of the variables included in the presented model. However, if substituted for the weight change variable in the progression model, change in BMI (P<.001), neck girth (P<.001), waist girth (P<.001), and total skinfold thickness (P = .05) were positively associated with change in the AHI. Baseline BMI was a significant predictor of AHI change (P = .01), independent of weight change. The regression coefficient (SE) of baseline BMI was 0.013 (0.005), indicating an expected increase of approximately 1% in the AHI for each increment of 1 kg/m2 in baseline BMI. No interaction terms between weight change and any other examined covariates, including baseline weight, were statistically significant.
Conditional logistic regression was used to estimate the within-participant relation between percentage weight gain and the odds of developing moderate-to-severe SDB. Table 3 provides odds ratios and confidence intervals for weight increases of 5%, 10%, and 20%, adjusting for changes in cigarette use. Adults experiencing a 10% weight gain were estimated to have 6 times the odds of being newly classified as having moderate-to-severe SDB at follow-up (AHI15 events/h) compared with those with stable body weight. For persons with AHI greater than 15 events/h at baseline, these odds ratios can be interpreted as the relative odds of reducing the AHI below 15 events/h associated with weight loss. Since the conditional logistic approach models intrasubject changes in the AHI, fixed characteristics, such as sex, are implicitly accounted for. There were no significant interactions between weight change and examined covariates.
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Table 3. Conditional Logistic Regression Coefficients and Odds Ratios* for Development of Moderate-to-Severe Sleep-Disordered Breathing (SDB) (AHI15 Events/h) for Selected Increments of Weight Gain
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COMMENT
In persons with SDB, we found a relation between weight gain and increased SDB severity. In persons who initially had mild or no SDB, we found weight gain predicted the development of moderate-to-severe SDB. Weight loss was associated with a reduced SDB severity and likelihood of developing SDB. These results were independent of many potential confounding factors, such as age, baseline body habitus measures, and change in smoking habits.
This prospective study benefited from a unique combination of features. It used a large population-based sample that provided more precise and generalizable results than previous clinic-based studies of weight loss and severe SDB in patients who were morbidly obese. Unlike those studies, this study was able to assess the relation between weight gain and SDB. This is an important advantage for public health interpretation of the study because of the increasing prevalence of obesity in the United States.28-30 This study also benefits from high-quality laboratory-based polysomnographic assessment of SDB, currently the diagnostic gold standard for SDB.
Our results are largely consistent with other research examining excess weight and its relation to SDB. Cross-sectional clinic-32-40 and population-based3-6,22, 41-44 investigations typically find significant correlations. Five small (n15) uncontrolled studies of surgical weight loss45-49 in patients who were severely obese found mean weight loss ranging from 25% to 50% of baseline weight yielded 70% to 98% mean reductions in indices of SDB. Eight small (n<30) uncontrolled studies of dietary weight loss49-55,58 in obese patients found that a range of 10% to 20% mean weight losses yielded mean 30% to 75% reductions in indices of SDB. Two controlled dietary weight loss studies56-57 found mean weight losses of 9% and 17% yielded mean AHI decreases of 47% and 61%, respectively. Two small (n55) longitudinal studies of SDB change in patients with sleep apnea63-64 found no statistically significant correlations between change in AHI and change in BMI. These null findings may be because of insufficient statistical power. Together, our longitudinal results, those from cross-sectional and weight-loss studies by other investigators, and biological plausibility provide evidence consistent with a causal link between excess body weight and SDB.
A variety of body habitus measures, including neck morphology3, 5-6,32-35,38, 43, 65 (neck girth or neck fat distribution), general obesity37, 39, 43 (BMI and skinfold measurements), and central obesity36, 39-40,43 (waist-to-hip ratio, waist girth, and abdominal visceral adiposity) have been cross-sectionally associated with SDB. Accordingly, we investigated changes in neck girth, waist-to-hip ratio, skinfold measurements, and BMI, as well as percentage body weight, as prospective predictors of SDB. We found that changes in percentage body weight predicted changes in AHI as well as those other measures and that our models were not substantially improved by the addition of other body habitus parameters. We chose to focus on weight change as the measure of change in body habitus, as it is a common and easily measured parameter.
Study limitations include incomplete follow-up of the eligible baseline sample. Twenty-seven percent of the baseline sample (258/948 baseline participants) either refused or were not reachable for follow-up. If the relation between body weight and SDB is substantially different in the entire baseline sample and the 690 participants examined for this study, we would be concerned about a bias in our results because of incomplete follow-up. As a check for such a discrepancy, we used linear regression to examine the baseline cross-sectional associations of log(AHI + 1) and log(weight), controlling for height, age, sex, current cigarette smoking status, and alcoholic drinks per week in the 2 samples. The baseline coefficient (SE) for log(weight) in this study's sample is 2.0 (0.2). The corresponding coefficient in the entire eligible, invited baseline sample is 2.1 (0.1). Here we find no substantial difference in the relation of SDB and weight in the samples. Although this does not rule out a longitudinal bias, it reduces concern that incomplete follow-up compromises our findings.
There are a few additional issues regarding our results that merit discussion. First, because few individuals in the sleep cohort experienced large percentage changes in weight, we do not recommend generalizing our findings to very large weight changes. Supplementary semiparametric spline modeling of our data indicated that within the range of ± 20% weight change, the association of weight change and SDB change (as characterized by our progression model) was well described by a linear function. However, the relation at greater weight change plateaued. Unfortunately, the number of participants with extreme weight change proved too few to carefully characterize associations that involved more than 20% weight change. Thus, these participants were excluded, and the findings presented in this report should not be extrapolated beyond 20% weight change.
Second, 43 (6%) of the participants had baseline and follow-up AHI equal to zero. This minority may represent persons with normal nocturnal breathing that is resistant to perturbation in the presence of weight change or other disturbance. Our progression model does not readily accommodate such persons.
Third, there is a substantial amount of variability in AHI change that is not accounted for in our final models. The residual variability is due to both factors other than weight change that impact SDB and measurement error in assessing SDB.
Fourth, we found no statistically significant evidence that the association between percentage weight change and SDB depended strongly on baseline habitus. However, in normal weight participants, the mean AHI was low and weight loss uncommon. Thus, we could not rigorously address the association of weight loss and reduced SDB severity in the normal weight participants with SDB.
Fifth, it is plausible that excess body weight acts either over time, by accelerating the progression of SDB, or acutely by rapidly modulating SDB through, for example, increased resistance to airflow via fat deposition in the proximity of the upper airway. With our study design, we were unable to determine to what extent one or both of these processes might be occurring. However, we found change in weight and baseline habitus (BMI in our progression model) independently predicted change in SDB severity, indicating that both an accelerated progression and short-term response might occur. Weight-loss studies that have demonstrated a reduction in SDB severity have tended to be short term, also suggesting that at least some of the response of SDB to excess weight is incurred almost simultaneously with weight change.
Finally, we do not know the causes of weight variations in participants whose weight did change and thus cannot specify the relative importance of weight change due to alterations in energy intake, physical activity, or metabolism. These last 2 issues point to the need for future longer-term follow-up studies to examine the relation between body habitus and SDB over decades, focusing on the effects of diet, exercise, and other related medical and lifestyle factors.
Obesity is a growing worldwide health problem, and its strong association with SDB is likely to be causal. It follows that the incidence of SDB will continue to grow in prominence and that clinical and public health strategies using weight control will be attractive approaches to the treatment of SDB. Our findings have important clinical implications for overweight patients with mild-to-moderate SDB who are poor candidates for nasal continuous positive airway pressure therapy. Weight loss may be appropriate as an alternative strategy for reduction in the severity and progression of SDB and for improvement in daytime symptoms. Furthermore, overweight people without overt clinical manifestations of SDB now have another incentive to lose weight or at least not to gain additional weight. Finally, these findings emphasize the importance of preventing weight gain in normal weight persons to avoid the development or progression of SDB.
AUTHOR INFORMATION
Funding/Support: This research was supported by grants R01HL62252, RR03186, and AG14124-05 from the National Institutes of Health.
Acknowledgment: Polysomnographic evaluation was conducted by the Sleep Laboratory Core of the University of Wisconsin Specialized Center of Research in Cardiopulmonary Disorders of Sleep. We thank Steven Weber, PhD, Laurel Finn, Tony Jacques, Linda Evans, and Kathy Pluff for their technical expertise.
Corresponding Author and Reprints: Paul E. Peppard, PhD, University of Wisconsin School of Medicine, Department of Preventive Medicine, 502 N Walnut St, Madison, WI 53705 (e-mail: ppeppard{at}facstaff.wisc.edu).
Author Affiliations: Departments of Preventive Medicine (Drs Peppard, Young, Palta, and Dempsey), Medicine (Dr Skatrud), and Biostatistics and Medical Informatics (Dr Palta), University of Wisconsin School of Medicine, Madison.
REFERENCES
| |
1. Kripke DF, Ancoli-Israel S, Klauber MR, Wingard DL, Mason WJ, Mullaney DJ. Prevalence of sleep-disordered breathing in ages 40-64 years: a population-based survey. Sleep. 1997;20:65-76.
ISI
| PUBMED
2. Bearpark H, Elliott L, Grunstein R, et al. Snoring and sleep apnea: a population study in Australian men. Am J Respir Crit Care Med. 1995;151:1459-1465.
ABSTRACT
3. Olson LG, King MT, Hensley MJ, Saunders NA. A community study of snoring and sleep-disordered breathing: prevalence. Am J Respir Crit Care Med. 1995;152:711-716.
ABSTRACT
4. Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328:1230-1235.
FREE FULL TEXT
5. Ferini-Strambi L, Zucconi M, Palazzi S, et al. Snoring and nocturnal oxygen desaturations in an Italian middle-aged male population: epidemiologic study with an ambulatory device. Chest. 1994;105:1759-1764.
FREE FULL TEXT
6. Stradling JR, Crosby JH. Predictors and prevalence of obstructive sleep apnoea and snoring in 1001 middle aged men. Thorax. 1991;46:85-90.
FREE FULL TEXT
7. Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J for the Cooperative Group Burgos-Santander. The association between sleep apnea and the risk of traffic accidents. N Engl J Med. 1999;340:847-851.
FREE FULL TEXT
8. Gottlieb DJ, Whitney CW, Bonekat WH, et al. Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. Am J Respir Crit Care Med. 1999;159:502-507.
FREE FULL TEXT
9. Engleman HM, Martin SE, Dreary IJ, Douglas NJ. Effect of CPAP therapy on daytime function on patients with mild sleep apnoea/hypopnea syndrome. Thorax. 1997;52:114-119.
ABSTRACT
10. Redline S, Strauss M, Adams N, et al. Neuropsychological function in mild sleep apnea. Sleep. 1997;20:160-167.
ISI
| PUBMED
11. Young T, Blustein J, Finn L, Palta M. Sleep-disordered breathing and motor-vehicle accidents in a population-based sample of employed adults. Sleep. 1997;20:608-613.
ISI
| PUBMED
12. Kim HC, Young T, Matthews CG, Weber SM, Woodard AR, Palta M. Sleep-disordered breathing and neuropsychological deficits. Am J Respir Crit Care Med. 1997;156:1813-1819.
FREE FULL TEXT
13. Finn L, Young T, Palta M, Fryback D. Sleep-disordered breathing and self-reported general health status in the Wisconsin Sleep Cohort Study. Sleep. 1998;21:701-706.
ISI
| PUBMED
14. Guilleminault C, Stoohs R, Kim Y, Chervin R, Black J, Clerk A. Upper airway sleep-disordered breathing in women. Ann Intern Med. 1995;122:493-501.
FREE FULL TEXT
15. Johns MW. Daytime sleepiness, snoring and obstructive sleep apnea. Chest. 1993;103:30-36.
FREE FULL TEXT
16. Grunstein RR, Stenlof K, Hedner J, Sjostrom L. Impact of obstructive sleep apnea and sleepiness on metabolic and cardiovascular risk factors in the Swedish Obese Subjects (SOS) Study. Int J Obes Relat Metab Disord. 1995;19:410-418.
ISI
| PUBMED
17. Koskenvuo M, Kaprio J, Partinen M, Langinvainio H, Sarna S, Heikkila K. Snoring as a risk factor for hypertension and angina pectoris. Lancet. 1985;1:893-896.
ISI
| PUBMED
18. Koskenvuo M, Kaprio J, Telakivi T, Partinen M, Heikkila K, Sarna S. Snoring as a risk factor for ischaemic heart disease and stroke in men. BMJ. 1987;294:16-19.
FREE FULL TEXT
19. Nieto FJ, Young TB, Lind BK, et al for the Sleep Heart Health Study. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. JAMA. 2000;283:1829-1836.
FREE FULL TEXT
20. Partinen M, Jamieson A, Guilleminault C. Long-term outcome for obstructive sleep apnea syndrome patients: mortality. Chest. 1988;94:1200-1204.
FREE FULL TEXT
21. Partinen M, Guilleminault C. Daytime sleepiness and vascular morbidity at seven-year follow-up in obstructive sleep apnea patients. Chest. 1990;97:27-32.
FREE FULL TEXT
22. Schmidt-Nowara WW, Coultas DB, Wiggins C, Skipper BE, Samet JM. Snoring in a Hispanic-American population: risk factors and association with hypertension and other morbidity. Arch Intern Med. 1990;150:597-601.
FREE FULL TEXT
23. Young T, Peppard P, Palta M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med. 1997;157:1746-1752.
FREE FULL TEXT
24. Hu FB, Willett WC, Colditz GA, et al. Prospective study of snoring and risk of hypertension in women. Am J Epidemiol. 1999;150:806-816.
FREE FULL TEXT
25. Hu FB, Willett WC, Manson JE, et al. Snoring and risk of cardiovascular disease in women. J Am Coll Cardiol. 2000;35:308-313.
FREE FULL TEXT
26. Lindberg E, Christer J, Svardsudd K, Gislason T, Jerker H, Boman G. Increased mortality among sleep snorers: a prospective population based study. Thorax. 1998;53:631-637.
FREE FULL TEXT
27. Peppard PE, Young TB, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342:1378-1384.
FREE FULL TEXT
28. Lewis CE, Jacobs DR Jr, McCreath H, et al. Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study: Coronary Artery Risk Development in Young Adults. Am J Epidemiol. 2000;151:1172-1181.
FREE FULL TEXT
29. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The spread of the obesity epidemic in the United States, 1991-1998. JAMA. 1999;282:1519-1522.
FREE FULL TEXT
30. Flegal KM, Carrol MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960-1994. Int J Obes Relat Metab Disord. 1998;22:39-47.
FULL TEXT
|
ISI
| PUBMED
31. Strobel RJ, Rosen RC. Obesity and weight loss in obstructive sleep apnea: a critical review. Sleep. 1996;19:104-115.
ISI
| PUBMED
32. Davies RJ, Stradling JR. The relationship between neck circumference, radiographic pharyngeal anatomy, and the obstructive sleep apnoea syndrome. Eur Respir J. 1990;3:509-514.
ABSTRACT
33. Katz I, Stradling J, Slutsky AS, Zamel N, Hoffstein V. Do patients with obstructive sleep apnea have thick necks? Am Rev Respir Dis. 1990;141:1228-1231.
ISI
| PUBMED
34. Hoffstein V, Mateika S. Differences in abdominal and neck circumferences in patients with and without obstructive sleep apnoea. Eur Respir J. 1992;5:377-381.
ABSTRACT
35. Davies RJ, Ali NJ, Stradling JR. Neck circumference and other clinical features in the diagnosis of the obstructive sleep apnoea syndrome. Thorax. 1992;47:101-105.
FREE FULL TEXT
36. Grunstein R, Wilcox I, Yang TS, Gould Y, Hedner J. Snoring and sleep apnoea in men: association with central obesity and hypertension. Int J Obes Relat Metab Disord. 1993;17:533-540.
ISI
| PUBMED
37. Levinson PD, McGarvey ST, Carlisle CC, Eveloff SE, Herbert PN, Millman RP. Adiposity and cardiovascular risk factors in men with obstructive sleep apnea. Chest. 1993;103:1336-1342.
FREE FULL TEXT
38. Shelton KE, Woodson H, Gay S, Suratt PM. Pharyngeal fat in obstructive sleep apnea. Am Rev Respir Dis. 1993;148:462-466.
ISI
| PUBMED
39. Millman RP, Carlisle CC, McGarvey ST, Eveloff SE, Levinson PD. Body fat distribution and sleep apnea severity in women. Chest. 1995;107:362-366.
FREE FULL TEXT
40. Shinohara E, Kihara S, Yamashita S, et al. Visceral fat accumulation as an important risk factor for obstructive sleep apnoea syndrome in obese subjects. J Intern Med. 1997;241:11-18.
FULL TEXT
|
ISI
| PUBMED
41. Jennum P, Hein HO, Suadicani P, Gyntelberg F. Cardiovascular risk factors in snorers: a cross-sectional study of 3,323 men aged 54 to 74 years: the Copenhagen male study. Chest. 1992;102:1371-1376.
FREE FULL TEXT
42. Jennum P, Sjol A. Snoring, sleep apnoea and cardiovascular risk factors: the MONICA II Study. Int J Epidemiol. 1993;22:439-444.
FREE FULL TEXT
43. Bearpark H, Elliot L, Grunstein R, et al. Occurrence and correlates of sleep disordered breathing in the Australian town of Busselton: a preliminary analysis. Sleep. 1993;16(suppl 8):S3-S5.
44. Enright PL, Newman AB, Wahl PW, Manolio TA, Haponik EF, Boyle PJ. Prevalence and correlates of snoring and observed apneas in 5,201 older adults. Sleep. 1996;19:531-538.
ISI
| PUBMED
45. Harman EM, Wynne JW, Block AJ. The effect of weight loss on sleep-disordered breathing and oxygen desaturation in morbidly obese men. Chest. 1982;82:291-294.
FREE FULL TEXT
46. Peiser J, Peretz L, Ovnat A, Charuzi I. Sleep apnea syndrome in the morbidly obese as an indication for weight reduction surgery. Ann Surg. 1984;199:112-125.
ISI
| PUBMED
47. Charuzi I, Ovnat A, Peiser J, Saltz H, Weitzman S, Lavie P. The effect of surgical weight reduction on sleep quality in obesity-related sleep apnea syndrome. Surgery. 1985;97:535-538.
ISI
| PUBMED
48. Pillar G, Peled R, Lavie P. Recurrence of sleep apnea without concomitant weight increase 7.5 years after weight reduction surgery. Chest. 1994;106:1702-1704.
FREE FULL TEXT
49. Rajala R, Partinen M, Sane T, Pelkonen R, Huikuri K, Seppalainen AM. Obstructive sleep apnoea syndrome in morbidly obese patients. J Intern Med. 1991;230:125-129.
ISI
| PUBMED
50. Rubinstein I, Colapinto N, Rotstein LE, Brown IG, Hoffstein V. Improvement in upper airway function after weight loss in patients with obstructive sleep apnea. Am Rev Respir Dis. 1988;138:1192-1195.
ISI
| PUBMED
51. Pasquali R, Colella P, Cirignotta F, et al. Treatment of obese patients with obstructive sleep apnea syndrome (OSAS): effect of weight loss and interference of otorhinolaryngoiatric pathology. Int J Obes. 1990;14:207-217.
ISI
| PUBMED
52. Suratt PM, McTier RF, Findley LJ, Pohl SL, Wilhoit SC. Effect of very-low-calorie diets with weight loss on obstructive sleep apnea. Am J Clin Nutr. 1992;56(suppl 1):182S-184S.
53. Kiselak J, Clark M, Pera V, Rosenberg C, Redline S. The association between hypertension and sleep apnea in obese patients. Chest. 1993;104:775-780.
FREE FULL TEXT
54. Nahmias J, Kirschner M, Karetzky MS. Weight loss and OSA and pulmonary function in obesity. N J Med. 1993;90:48-53.
PUBMED
55. Noseda A, Kempenaers C, Kerkhofs M, Houben JJ, Linkowski P. Sleep apnea after 1 year domiciliary nasal-continuous positive airway pressure and attempted weight reduction: potential for weaning from continuous positive airway pressure. Chest. 1996;109:138-143.
FREE FULL TEXT
56. Smith PL, Gold AR, Meyers DA, Haponik EF, Bleecker ER. Weight loss in mildly to moderately obese patients with obstructive sleep apnea. Ann Intern Med. 1985;103:850-855.
ISI
| PUBMED
57. Schwartz AR, Gold AR, Schubert N, et al. Effect of weight loss on upper airway collapsibility in obstructive sleep apnea. Am Rev Respir Dis. 1991;144:494-498.
ISI
| PUBMED
58. Suratt PM, McTier RF, Findley LJ, Pohl SL, Wilhoit SC. Changes in breathing and the pharynx after weight loss in obstructive sleep apnea. Chest. 1987;92:631-637.
FREE FULL TEXT
59. Lohman TG, Roche AF, Martorell R. Measurement descriptions and techniques. In: Lohman TG, Roche AF, Martorell R, eds. Anthropometric Standardization Reference Manual. Champaign, Ill: Human Kinetics Publishers; 1988:1-55.
60. Rechtschaffen A, ed, Kales A, ed. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Washington, DC: Government Printing Office; 1968. NIH publication 204.
61. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia, Pa: Lippincott-Raven Publishers; 1998.
62. Wetter DW, Young TB, Bidwell TR, Badr MS, Palta M. Smoking as a risk factor for sleep-disordered breathing. Arch Intern Med. 1994;154:2219-2224.
FREE FULL TEXT
63. Sforza E, Addati G, Cirignotta F, Lugaresi E. Natural evolution of sleep apnoea syndrome: a five year longitudinal study. Eur Respir J. 1994;7:1765-1770.
ABSTRACT
64. Pendlebury ST, Pepin JL, Veale D, Levy P. Natural evolution of moderate sleep apnoea syndrome: significant progression over a mean of 17 months. Thorax. 1997;52:872-878.
ABSTRACT
65. Mortimore IL, Marshall I, Wraith PK, Sellar RJ, Douglas NJ. Neck and total body fat deposition in nonobese and obese patients with sleep apnea compared with that in control subjects. Am J Respir Crit Care Med. 1998;157:280-283.
FREE FULL TEXT
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