Context Sleep-disordered breathing (SDB) is both prevalent and associated with
serious chronic illness. The incidence of SDB and the effect of risk factors
on this incidence are unknown.
Objective To determine the 5-year incidence of SDB overall and as influenced by
risk factors.
Design, Setting, and Participants Of the 1149 participants in the Cleveland Family Study, those aged 18
years or older, from either case or control families, who had 2 in-home sleep
studies 5 years apart. The first had to have been performed before June 30,
1997, and had to have normal results (apnea hypopnea index [AHI] <5). Data
included questionnaire information on medical and family history, SDB symptoms;
measurement of height, weight, blood pressure, waist and hip circumference,
and serum cholesterol concentration; and overnight sleep monitoring.
Main Outcome Measure Apnea hypopnea index, defined as number of apneas and hypopneas per
hour of sleep. Sleep-disordered breathing was defined by an AHI of at least
10 (mild to moderate) or of at least 15 (moderate).
Results Forty-seven (16%) of 286 eligible participants, (95% confidence interval
[CI], 13%-21%) had a second-study AHI of at least 10 and 29 (10%) participants
(95% CI, 7%-14%) had a second-study AHI result of at least 15. For the AHI
results of at least 15, we estimate that about 2.5% may represent test variability.
By ordinal logistic regression analysis, AHI was significantly associated
with age (odds ratio [OR] per 10-year increase, 1.79; 95% CI, 1.41-2.27),
body mass index (BMI; OR per 1-unit increase, 1.14; 95% CI, 1.10-1.19), sex
(OR for men vs women, 4.12; 95% CI, 2.29-7.43), waist-hip ratio (OR per 0.1
unit increase, 1.61; 95% CI, 1.04-2.28), and serum cholesterol concentration
(OR per 10-mg/dL [0.25-mmol/L] increase, 1.11; 95% CI, 1.03-1.19). Interactions
were noted between age and both sex (P = .003) and
BMI (P = .05). The OR for increased AHI per 10-year
age increase was 2.41 in women (95% CI, 1.78-3.26) and 1.15 in men (95% CI,
0.78-1.68), with the male vs female OR decreasing from 5.04 (95% CI, 2.19-11.6)
at age 30 years to 0.54 (95% CI, 0.15-1.99) at age 60 years. The OR for increased
AHI per 1-unit increase in BMI decreased from 1.21 (95% CI, 1.11-1.31) at
age 20 years to 1.05 (95% CI, 0.96-1.15) at age 60 years.
Conclusions The 5-year incidence is about 7.5% for moderately severe SDB and 16%
(or less) for mild to moderately severe SDB. Incidence of SDB is influenced
independently by age, sex, BMI, waist-hip ratio, and serum cholesterol concentration.
Predominance in men diminishes with increasing age, and by age 50 years, incidence
rates among men and women are similar. The effect of BMI also decreases with
age and may be negligible at age 60 years.
Numerous studies have demonstrated that sleep-disordered breathing (SDB)
is both a prevalent phenomenon1 and associated
with or causal of serious chronic illness.2 Current
treatments seem to halt the progression or even reverse some deleterious effects
of SDB.3-5 If these
therapies are to be made available to larger liable populations, precise estimates
of both prevalence and incidence rates of SDB and its sequelae must be developed.
Cross-sectional studies have derived estimates of the prevalence of SDB in
a number of populations, particularly of adults.6,7 Incidence
data are equally important in anticipating the scale of effects of SDB and
of resources to treat patients; however, no studies have examined the incidence
of SDB or the influence of risk factors on the incidence of SDB.
The Cleveland Family Study was established both to examine causal factors
and to document the natural history of SDB. As part of this effort, sleep
studies have been carried out several times on a portion of the participants.
This affords the opportunity to determine the incidence of SDB. We have also
assessed the relative role of risk factors in determining the incidence of
SDB. The results of these studies, previously reported in abstract form,8 are reported in detail herein.
The characteristics of the case and community control populations that
reside in the greater Cleveland area have been described in detail.9,10 Of the entire population, 1149 individuals,
aged 18 years or older, members of either case families with at least 1 member
with polysomnographically confirmed SDB or of neighborhood control families,
had at least 1 evaluation and in-home sleep study before June 30, 1997 (Figure 1). These family members
included relatives and spouses of affected probands or neighborhood controls.
A portion of these individuals also had undergone a second sleep study approximately
5 years later. From these, participants who were free of SDB on the initial
polysomnogram (apnea hypopnea index [AHI] <5) were selected for this study.
Participants were studied in their homes, using nearly identical protocols
at both examinations. Medical history, medication use, family history, ethnicity,
and symptoms of SDB were assessed with the Sleep and Health Questionnaire.11 A limited physical examination was performed by the
research technician, including measurement of height, weight and circumferences
of waist and hips, and visual assessment of the presence and degree of tonsillar
hypertrophy or upper airway obstruction.12 Blood
pressure (Korotkoff-1 and Korotkoff-5 sounds) was measured twice in the sitting
position after at least 5 minutes of rest, using a size-appropriate cuff.
For discrepant results (≥4 mm Hg discordance), a third reading was obtained.
Serum total cholesterol concentration was measured without regard to the last
meal. Overnight in-home sleep monitoring was performed with an Edentrace I
or II monitor (Eden Prairie, Minn) as described.13 Respiratory
events were defined as cessations (apneas) or discrete
reductions (hypopneas) in airflow or chest wall impedance,
lasting at least 10 seconds and associated with a fall in oxygen saturation
of at least 2.5%. The AHI was determined by dividing the number of respiratory
events by the estimated hours of sleep.
Data on the covariates that were examined in relation to the incidence
of SDB were obtained from the first examination whenever possible. The exceptions
were waist and hip circumferences and serum cholesterol concentrations, which
were measured at the second examination. Systemic hypertension was defined
as a systolic blood pressure of at least 140 mm Hg or a diastolic blood pressure
of at least 90 mm Hg by measurement, or by a self-reported use of antihypertensive
medication. Cardiovascular disease or diabetes mellitus was defined by a self-reported
history of heart attack, angina pectoris, stroke, heart failure, cardiac arrhythmia,
or physician-diagnosed diabetes mellitus. Criteria for smoking included either
ever smoking or currently smoking (≥1 cigarette daily during the month
preceding the visit). Use of alcohol was defined by consuming an alcoholic
beverage more than once weekly. A positive family history included multiple
(≥2) relatives with AHI values of at least 15.
Incident SDB was based on 2 definitions. An AHI of at least 15 was used
to identify at least moderately severe SDB, according to the definition of
an American Academy of Sleep Medicine task force.14 An
AHI of at least 10 was also examined as an indicator of mild SDB. Change in
AHI from less than 5 to a level of 5 to 9.9 may indicate progression of SDB.
We did not include such change in the definition of incidence, however, because
small changes are likely to be in the range of night-to-night variability.15
The protocol was approved by the institutional review boards of the
local hospitals from which the probands were recruited. Written informed consent
was obtained for all participants.
Data were analyzed using SAS version 8.2 (SAS Inc, Cary, NC). To account
for familial clustering, univariate between-group comparisons of continuous
variables were made using mixed models, with family as a random effect; comparisons
of binary outcomes were made using generalized estimating equation (GEE) logistic
regression models.16 Multivariate analyses
were performed using ordinal logistic regression under a proportional odds
model, for which AHI at the second visit was grouped in 4 ordered categories
(<5, 5-9.9, 10-14.9, ≥15). This approach simultaneously models 3 cumulative
logits corresponding to using binary cut points at 5, 10, and 15, written
as log {Pr(AHI≥5)/Pr(AHI<5)}, log{Pr(AHI≥10)/Pr(AHI<10)}, and
log{Pr(AHI)≥15/Pr(AHI<15)}, respectively. Preliminary analyses and model
building were done ignoring the familial clustering. In the final models,
we adjusted for familial clustering using the GEE approach, using the SAS
macro GEECAT program of Williamson et al17 with
an independence working correlation structure. Under this proportional odds
model, 1 parameter is estimated for each predictor in the model. The parameter
represents the effect of a 1-unit increase in the predictor variable on the
logit (log odds), which is assumed to be the same for all 3 logits. A score
test was used to verify the proportional odds assumption in the final model.18 To further verify the proportional odds model, we
fit binary GEE logistic regressions using AHI cutoffs of at least 5, at least
10, and at least 15, and compared the results with those of the ordinal regressions.
The modeling strategy was to first enter age, sex, and body mass index (BMI),
calculated as weight in kilograms divided by the square of height in meters.
Explanatory covariates (waist-hip ratio, smoking history, etc) were then tested
individually when added in a stepwise fashion to the model with age, sex,
and BMI. Variables were retained in the model if the likelihood ratio test
was significant at P<.05. Interactions among the
final covariates were then tested. Because of the smaller size of the subset
in which serum cholesterol was measured (cholesterol subsample), findings
were repeated both with and without cholesterol and the parameter estimates
were compared.
Of the 1149 individuals aged 18 years or older at the time of their
initial sleep study, 613 were found to have an AHI of at least 5. As noted
in Figure 1, 286 of the remaining
536 participants were followed up with a second evaluation and in-home sleep
study and serve as the study population for this investigation. Two hundred
fifty participants met the inclusion criteria but were not followed up. Serum
cholesterol concentrations were measured for 218 of these participants. Sixty-eight
participants did not have their cholesterol concentrations measured because
of the following reasons: refused venipuncture, were studied before we began
collecting blood samples, or were evaluated at a time when the phlebotomist
was unavailable.
Baseline characteristics of the participants who are studied herein
(entire subsample) and those who were not followed up (eligible participants
not studied) are compared in Table 1.
These groups differed slightly but significantly in mean BMI and in percentage
that were women but not in other comparisons. Of the 286 participants in the
entire subsample, 132 (46%) were ever smokers, 77 (27%) were current smokers,
37 (13%) were currently taking antihypertensive medication, 66 (23%) used
alcohol currently, 89 (31%) had large tonsils that appeared to crowd the oral
airway, and 123 (43%) had a family history of SDB. A small difference in percentage
of women existed in the smaller cholesterol subsample (n = 218) relative to
either the entire subsample or the eligible participants who were not studied
(Table 1).
Comparisons of participants in case vs control families are shown in Table 2. The sample included 198 members
of case families and 88 members of control families. Virtually no differences
were noted between case and control groups in the incidence of AHI of at least
10 (16% vs 18%, respectively) or of at least 15 (10% vs 10%, respectively).
Similar relationships were observed between sex or age and incident SDB for
members of case and control families (Table
2). Thus, data from the case and control participants were combined
for subsequent analyses.
At the second visit, 181 of the 286 participants (63%) retained AHI
values of less than 5 and thus were not considered to have SDB (Table 3). Forty-seven participants were newly found to have an AHI
of at least 10 on the second sleep study. Sixteen percent of participants
(95% confidence interval [CI], 13%-21%) had AHI values of at least 10, and
10% (95% CI, 7%-14%) had AHI values of at least 15. These percentages represent
the upper bound of the 5-year incidence of mild and/or moderately severe SDB
and moderately severe SDB, respectively.
Factors that may modify the incidence rates of SDB are examined in Table 4 by means of univariate statistics.
Incidences varied in a statistically significant manner with several factors:
male sex, increasing initial age, BMI, and second-visit waist-hip ratio. A
trend of increase in SDB with the higher quartiles of cholesterol levels and
presence of cardiovascular disease (including diabetes mellitus) or hypertension
was observed. The incidence of SDB did not appear to be influenced by race,
large tonsils, smoking, alcohol intake, or family history of SDB.
The relative importance of the covariates on the incidence of SDB was
examined by ordinal logistic regression analysis, deriving odds ratios (ORs)
at an AHI of at least 5, at least 10, and at least 15 compared with those
of less than 5, less than 10, and less than 15, respectively (Table 5). The OR for each covariate was adjusted for the effects
of the other significant covariates. In analyses of the entire subsample,
age, BMI, and sex were significant covariates. Waist-hip ratio was independently
associated with AHI (OR, 1.61, 95% CI, 1.04-2.48). Of note, adjusting for
waist-hip ratio reduced the OR of sex from 4.12 to 2.65. In the cholesterol
subsample, serum cholesterol concentration was significantly associated with
AHI (OR, 1.11 for each 10-mg/dL [0.26-mmol/L] increase). The ORs for the other
covariates were changed minimally with inclusion of cholesterol as a covariate.
Variables that were not significant in these analyses included self-reported
cardiovascular disease or diabetes, family history, race, smoking, alcohol
ingestion, and tonsillar size. Hypertension, which was marginally significant
in the univariate analysis, was highly associated with age, BMI, and increased
cholesterol concentration. The significance of its association with AHI was
inconsistent, and it was not included in final models. Separate binary logistic
regression analyses yielded ORs that are very similar to those of Table 5. Tests of the proportional odds
assumption were not significant, indicating that the proportional odds model
was appropriate for these data.
We further explored potential variation in effects across population
subgroups with the use of interaction terms in the regression models. Both
age and sex and age and BMI interacted in a significant manner (P = .003 and .05, respectively). No other interactions, including those
involving waist-hip ratio (for example, sex*waist-hip ratio), were statistically
significant. The effect of these interactions on ORs is provided in Table 6 and Figure 2. The odds for increased AHI increases by about 140% per
10-year increment in women (OR, 2.41) but by only 15% in men (OR, 1.15). This
results in a curvilinear decrease in the preponderant association of male
sex with SDB with increasing age, approaching unity at about age 50 years
(Figure 2A). In other words, there
is little or no apparent increased risk of incident SDB associated with male
sex after age 50 years. The influence of BMI on AHI decreases with age, approaching
unity after age 60 years (Figure 2B).
Both waist-hip ratio and serum cholesterol concentration remain significant
covariates.
The Cleveland Family Study was undertaken to provide added definition
of the natural history of SDB and to identify and quantitate the importance
of risk factors that predispose individuals to this syndrome. Although index
cases with polysomnographically proven SDB were identified in local sleep
laboratories, these individuals most importantly served as an entree to the
proband's family and also to control families. The members of these families
represent a relatively young urban population. Our previous studies in this
population have demonstrated the familial aggregation of and inherited predisposition
to SDB.9,10 Except for the selection
bias resulting from this familial aggregation in families of index cases,
this population would appear to have no special liability for the development
of SDB.
In our analyses, we have selected a subgroup from the Cleveland Family
Study in which to document the incidence of SDB over a period of nominally
5 years. Adults (aged ≥18 years), selected from both case and control families,
had an AHI of less than 5 at the study's start. Despite the differences in
the methods for selecting case and control families, we were unable to find
any differences between them that might bias our incidence data (Table 2). We thus believed ourselves justified
in combining the data from both groups for these analyses. The overall study
population was slightly more obese, was predominantly white and female but
was otherwise representative of the total population that was eligible for
2 in-home sleep studies (Table 1).
Participants were often cigarette smokers; consumed alcohol; or had a chronic
illness, such as hypertension, cardiovascular disease, and/or diabetes mellitus.
We found that the overall 5-year incidence of SDB is 10% for those with
an AHI of at least 15 and 16% for those with an AHI of at least 10 (Table 3). We know of no other studies that
have directly evaluated the incidence of SDB. Data bearing on prevalence are
available from a number of sources. These data are subject to variation according
to the nature of the population under study, the method of ascertainment,
and the definition of SDB. Most studies of sleep apnea, using a 2-tiered method
of ascertainment (usually questionnaire followed by sleep monitoring), yield
prevalence estimates on the order of 1% to 4% in general populations of adults.1,7,19-21 In
the Cleveland Family Study of the same population of adult subjects represented
in our analysis, we documented a prevalence of SDB (AHI≥15) of 10.5% on
an initial in-home sleep study, and 16.3% on a second study about 5 years
later.22 Relatively similar prevalences were
found by Peppard et al23 (7% and 10% prevalence
rates in middle-aged adults studied twice at a 4-year interval), and by Duran
et al24 (14% for men, 7% for women aged 30-70
years). In both studies, SDB was defined by an AHI of at least 15 but with
a 4% oxygen desaturation threshold for identification of apneic events (as
opposed to a 2.5% threshold in our study). Substantially higher prevalence
rates of SDB (>25%) have been reported in populations of elderly individuals.25
One might expect that a 5-year incidence of 10% would lead to a considerably
higher long-term prevalence (ie, in older individuals) than the 16% that our
data document.22 Certainly the prevalence of
SDB does increase with age,12 but probably
not to the degree suggested by the current incidence figure. Several reasons
may explain this paradox. One must consider the possibility of measurement
variability or of amelioration of SDB over time. Data bearing on these concepts
are meager. In a study that is of much shorter duration, Quan et al15 determined the AHI from 2 in-home sleep studies approximately
4 months apart in 91 subjects from the Sleep Heart Health Study, with the
finding of a high degree of concordance in the 2 evaluations. The intraclass
correlation coefficient for AHI between the 2 examinations was 0.81 (95% CI,
0.72-0.87). In 82% of subjects, the second respiratory disturbance index remained
either less than 15 or 15 or higher. Further analyses of these data show that
of 35 participants with an AHI of at least 15 on the first examination, none
had an AHI lower than 5 on second examination (S.R., unpublished data, 2003).
Similarly, in our own cohort, of the 48 adults with an initial AHI of at least
15 who underwent a repeat in-home sleep study about 5 years later, 2 subjects
(4%) had second AHI values of less than 5. These results suggest that only
a small proportion (approximately 2.5%) of individuals regress on second study
from an AHI of at least 15 to an AHI of less than 5. Thus, the overall 5-year
incidence rate of SDB is reduced from 10% to about 7.5% for an AHI of at least
15. We do not have information on which to base a similar estimate of test
variability for an AHI of at least 10. Another possible explanation for any
discrepancy between incidence and prevalence rates is that SDB accelerates
mortality in older individuals who have SDB for prolonged periods, perhaps
by facilitating the development of coronary artery disease, systemic hypertension
and its sequelae, or diabetes mellitus. Although this is a controversial subject,
a number of publications indicate that an acceleration in mortality may occur.26,27
Since the prevalence of SDB is influenced by either proven risk factors
(obesity, sex, age, family history) or less well-established associations
(cardiovascular disease, hypertension, diabetes mellitus),12,19,28-30 we
postulated that incidence may be affected in a similar manner. We examined
this relationship with data primarily from each participant's initial visit,
anticipating that early data may even be predictive of the development of
SDB. The serum cholesterol and waist-hip ratio measurements were only available
from the second visit, however. Epidemiologic experience with serum cholesterol
concentrations has demonstrated a high correlation among repeat measurements
over many years. For example, the product-moment correlation coefficients
over 6 years of repeated serum cholesterol concentrations from more than 1000
participants in the Framingham Study were approximately 0.7.31 A
similar correlation obtained for waist-hip ratio, a measure of body fat distribution
particularly in the abdomen.32 In 691 male
members of a Swedish birth cohort, the Spearman correlation coefficient for
waist-hip ratio at age 43 vs age 37 years was 0.6633 (K.
M. Henriksson, written communication, September 27, 2002). These data suggest
that measures of cholesterol and waist-hip ratio are moderately to strongly
correlated over several years, supporting the use of second-visit data as
surrogate predictive factors.
The initial analyses, using univariate statistics, suggested that incidence
varied significantly with sex, age, BMI, and waist-hip ratio, and possibly
the presence of chronic illness (cardiovascular disease, diabetes mellitus,
and hypertension) and casual serum cholesterol concentration (Table 4). Examining these associations by a multivariate procedure
(ordinal logistic regression), we found that age, BMI, sex, and waist-hip
ratio remained significant predictors of AHI (Table 5, entire subsample). Waist-hip ratio reduced the OR for sex
by about 35% (from 4.12 to 2.65), suggesting that some of the association
between sex and SDB is mediated by body fat distribution.32 Serum
cholesterol added to the regression was also significantly associated with
AHI and had little effect on the ORs of the other covariates (Table 5, cholesterol subsample). Results were similar with binary
logistic regression. Age, BMI, sex, waist-hip ratio, and serum cholesterol
concentration are all significant predictors of AHI.
The associations of age, sex, and obesity with SDB have been well documented,
not only in the Cleveland Family Study but also in many other investigations.1,9,20,24,28,34 Thus,
our finding strong direct associations of these covariates with the incidence
of SDB is not surprising. Other factors that are associated in a possibly
causal manner with the presence of SDB were not associated with the incidence
of SDB in the present study. In previous reports from the Cleveland Family
Study, we documented a strong association of race and family history with
SDB.9,12 The effect of race was
seen in black participants who were younger than 25 years. Since most members
of the population in these analyses were older than 25 years, the lack of
a race effect is understandable. The absence of an association of family history
may be the result of our necessarily restricting the analyses to individuals
who did not have SDB at their initial examination.
The effects of sex, age, and BMI on SDB are interrelated in a complex
manner, resulting in major modification of the incidence in special circumstances.
The incidence in men is, overall, greater than that in women (Table 4). With age, this risk in men increases only modestly, whereas
that risk in women increases steadily and markedly (Table 6). The result is that by age 50 years, sex differences in
the incidence of SDB essentially disappear (Table 6, Figure 2A). Changes
in sex hormone economy (metabolism distribution, clearance, use) have been
suggested as a cause of the increased prevalence of SDB in perimenopausal
women. This suggestion has been strengthened by the recent finding in the
Sleep Heart Health Study that hormone replacement therapy is associated with
lower AHI levels in postmenopausal women.35 The
AHI also increases directly with increasing BMI, a measure of overall obesity
(Table 5). Interestingly, the
effects of BMI diminish with increasing age (Table 6, Figure 2B), a
phenomenon that has been noted previously.36 Perhaps
other factors (change in ventilatory control, upper airway stability, etc)
assume a greater relative importance with age.
The mechanism by which serum total cholesterol concentration might influence
the incidence of SDB is unknown. Sleep disordered breathing is associated
with a predilection for cardiovascular disease, possibly because individuals
with SDB have augmented cardiovascular risk factors.29,37 For
example, Newman et al29 recently showed that
community-based subjects with AHIs in the highest population quartiles had
worse cardiovascular disease risk profiles (blood pressure, serum cholesterol
concentrations, and also waist-hip ratios) than those with lower AHIs. Obesity,
clearly a risk factor for SDB, is associated with insulin resistance, glucose
intolerance, and disordered lipid homeostasis.38 Possibly
the lipid (cholesterol) abnormalities predispose to SDB by mechanisms that
are independent of obesity. Kadotani et al39 have
described an association between apolipoprotein E ∊4 and SDB, but this
association has not been confirmed by others.40 Given
mounting evidence for a genetic basis for SDB,41 these
data suggest that a cardiovascular disease–SDB phenotype may be a manifestation
of common genetic risk factors (involving primary genes or modifier genes).
If so, individuals with elevated cholesterol levels may represent a phenotype
with augmented risk for progression of SDB.
We must consider how representative this population is of ambulatory
communities in the United States in general in order to extrapolate our findings.
The study population is well represented in black individuals proportionately
to their representation in the overall population of the United States. One
must still be cautious, however, in applying the overall findings to a group
that comprised only about 20% of the study population. Moreover, ethnic groups
other than whites and blacks were not represented in the participants. Otherwise,
members of control families were not knowingly selected for any medical condition,
including SDB. Our studying members of case families might bias the incidence
of or the effects of risk factors for SDB. Because of the familial nature
of SDB, for example, the incidence of SDB in relatives of probands with SDB
may be augmented. Our detailed comparison of the data from the case vs the
control populations does not identify any significant difference in incidence,
however. Moreover, logistic regression analyses failed to identify family
history as a risk factor for incident SDB. The fact that the population in
this study was slightly weighted toward both women and individuals with higher
BMI values, relative to others in this sample of family members who were not
studied, may distort the general applicability of these results.
In summary, we found the 5-year incidence of SDB in a community-based
sample of adults aged 18 years or older to be 16% or less for mild to moderately
severe SDB and to be about 7.5% for moderately severe SDB. Incidence was influenced
by age, sex, BMI, waist-hip ratio, and serum cholesterol concentration, either
singly or (for age-sex and age-BMI) in combination. We believe that these
findings are applicable to many populations and may ultimately be important
in framing the public health impact of SDB. We await the results of studies
by others to place our findings in proper perspective.
1.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.Google Scholar 2.Leung RS, Bradley TD. Sleep apnea and cardiovascular disease.
Am J Respir Crit Care Med.2001;164:2147-2165.Google Scholar 3.Pepperell JCT, Ramdassingh-Dow S, Crosthwaite N.
et al. Ambulatory blood pressure after therapeutic and subtherapeutic nasal
continuous positive airway pressure for obstructive sleep apnoea: a randomised
parallel trial.
Lancet.2002;359:204-210.Google Scholar 4.Alchanatis M, Tourkohoriti G, Kakouros S, Kosmas E, Podaras S, Jordanoglou JB. Daytime pulmonary hypertension in patients with obstructive sleep apnea.
Respiration.2001;68:566-572.Google Scholar 5.Javaheri S. Effects of continuous positive airway pressure on sleep apnea and ventricular
irritability in patients with heart failure.
Circulation.2000;101:392-397.Google Scholar 6.Bresnitz EA, Goldberg R, Kosinski RM. Epidemiology of obstructive sleep apnea.
Epidemiol Rev.1994;16:210-227.Google Scholar 7.Ip M, Lam B, Lauder IJ.
et al. A community study of sleep-disordered breathing in middle-aged Chinese
men in Hong Kong.
Chest.2001;119:62-69.Google Scholar 8.Redline S, Larkin E, Schluchter M.
et al. Incidence of sleep disordered breathing (SDB) in a population-based
sample [abstract].
Sleep.2001;24:A294.Google Scholar 9.Redline S, Tishler PV, Tosteson TD.
et al. The familial aggregation of obstructive sleep apnea.
Am J Respir Crit Care Med.1995;151:682-687.Google Scholar 10.Buxbaum SG, Elston RC, Tishler PV, Redline S. Genetics of the apnea hypopnea index in Caucasians and African Americans,
I: segregation analysis.
Genet Epidemiol.2002;22:243-253.Google Scholar 11.Kump S, Whalen C, Tishler PV.
et al. Assessment of the validity and utility of a sleep-symptom questionnaire.
Am J Respir Crit Care Med.1994;150:735-741.Google Scholar 12.Redline S, Tishler PV, Hans MG, Tosteson TD, Strohl KP, Spry K. Racial differences in sleep-disordered breathing in African-Americans
and Caucasians.
Am J Respir Crit Care Med.1997;155:186-192.Google Scholar 13.Redline S, Tosteson T, Boucher MA, Millman RP. Measurement of sleep-related breathing disturbances in epidemiologic
studies: assessment of the validity and reproducibility of a portable monitoring
device.
Chest.1991;100:1281-1286.Google Scholar 14. Sleep-related breathing disorders in adults: recommendations for syndrome
definition and measurement techniques in clinical research: the Report of
an American Academy of Sleep Medicine Task Force.
Sleep.1999;22:667-689.Google Scholar 15.Quan SF, Griswold ME, Iber C.
et al. Short-term variability of respiration and sleep during unattended non-laboratory
polysomnography: the Sleep Heart Health Study.
Sleep.2002;25:843-849.Google Scholar 16.Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models.
Biometrika.1986;73:13-22.Google Scholar 17.Williamson JM, Lipsitz SR, Kim KM. GEECAT and GEECOR: computer programs for the analysis of correlated
categorical response data.
Comput Methods Programs Biomed.1999;58:25-34.Google Scholar 18.Stokes ME, Davis CS, Koch GG. Categorical Data Analysis Using the SAS System. 2nd ed. Cary, NC: SAS Institute Inc; 2000.
19.Bixler EO, Vgontzas AN, Ten Have T, Tyson K, Kales A. Effects of age on sleep apnea in men.
Am J Respir Crit Care Med.1998;157:144-148.Google Scholar 20.Bixler EO, Vgontzas AN, Lin H-M. Prevalence of sleep-disordered breathing in women: effects of gender.
Am J Respir Crit Care Med.2001;163:608-613.Google Scholar 21.Gislason T, Almqvist M, Eriksson G, Taube A, Boman G. Prevalence of sleep apnea syndrome among Swedish men: an epidemiologic
study.
J Clin Epidemiol.1988;41:571-576.Google Scholar 22.Redline S, Schluchter MD, Larkin E.
et al. Association of progressive sleep disordered breathing (SDB) with cardiovascular
disease (CVD) risk factors [abstract].
Am J Respir Crit Care Med.2001;163:A37.Google Scholar 23.Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal study of moderate weight change and sleep-disordered breathing.
JAMA.2000;284:3015-3021.Google Scholar 24.Duran J, Esnaola S, Rubio R, Iztueta A. Obstructive sleep apnea-hypopnea and related clinical features in a
population-based sample of subjects aged 30 to 70 years.
Am J Respir Crit Care Med.2001;163:685-689.Google Scholar 25.Asplund R. Sleep disorders in the elderly.
Drugs Aging.1999;14:91-103.Google Scholar 26.Lavie P, Herer P, Peled R.
et al. Mortality in sleep apnea patients: a multivariate analysis of risk
factors.
Sleep.1995;18:149-157.Google Scholar 27.Mooe T, Franklin KA, Holmstrom K, Rabben T, Wiklund U. Sleep-disordered breathing and coronary artery disease: long-term prognosis.
Am J Respir Crit Care Med.2001;164:1910-1913.Google Scholar 28.Redline S, Kump K, Tishler PV, Browner I, Ferrette V. Gender differences in sleep disordered breathing in a community-based
sample.
Am J Respir Crit Care Med.1994;149:722-726.Google Scholar 29.Newman AB, Nieto FJ, Guidry U.
et al. for the Sleep Heart Health Study Research Group. Relation of sleep-disordered breathing to cardiovascular disease risk
factors: the Sleep Heart Health Study.
Am J Epidemiol.2001;154:50-59.Google Scholar 30.Ficker JH, Dertinger SH, Siegfried W.
et al. Obstructive sleep apnoea and diabetes mellitus: the role of cardiovascular
autonomic neuropathy.
Eur Respir J.1998;11:14-19.Google Scholar 31.Gordon T, Shurtleff D. Means at each examination and inter-examination variation of specified
characteristics: Framingham Study, exam 1 to exam 10. In: Kannel WB, Gordon T, eds. The Framingham Study:
An Epidemiological Investigation of Cardiovascular Disease. Washington,
DC: Dept of Health Education and Welfare; 1973. Publication No. NIH 74-478,
Section 29.
32.Pouliot MC, Despres JP, Lemieux S.
et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric
indexes of abdominal visceral adipose tissue accumulation and related cardiovascular
risk in men and women.
Am J Cardiol.1994;73:460-468.Google Scholar 33.Henriksson KM, Lindblad U, Agren B, Nilsson-Ehle P, Rastam L. Association between body height, body composition and cholesterol levels
in middle-aged men: the Coronary Risk Factor Study in Southern Sweden (CRISS).
Eur J Epidemiol.2001;17:521-526.Google Scholar 34.Redline S, Tosteson T, Tishler PV, Carskadon MA, Millman RP. Studies in the genetics of obstructive sleep apnea: familial aggregation
of symptoms associated with sleep-related breathing disturbances.
Am Rev Respir Dis.1992;145:440-444.Google Scholar 35.Sahar E, Redline S, Young T.
et al. Hormone replacement therapy and sleep disordered breathing.
Am J Respir Crit Care Med.2003. In press.Google Scholar 36.Redline S. Age-related differences in sleep apnea: generalizability of findings
in older populations. In: Kuna ST, Suratt PM, Remmers, JE, eds. Sleep
and Respiration in Aging Adults. New York, NY: Elsevier; 1991:189-194.
37.Shahar E, Whitney CW, Redline S.
et al. for the Sleep Heart Health Study Research Group. Sleep-disordered breathing and cardiovascular disease: cross-sectional
results of the Sleep Heart Health Study.
Am J Respir Crit Care Med.2001;163:19-25.Google Scholar 38.Flier JS, Foster DW. Eating disorders: obesity, anorexia nervosa and bulimia nervosa. In: Foster DW, Kronenberg HM, Larson PR, eds. Williams Textbook of Endocrinology. 9th ed. Philadelphia, Pa: WB Saunders;
1998:1061-1097.
39.Kadotani H, Kadotani T, Young T. Association between apolipoprotein E ∊4 and sleep-disordered breathing
in adults.
JAMA.2001;285:2888-2890.Google Scholar 40.Saarelainen S, Lehtimaki T, Kallonen E, Laasonen K, Poussa T, Nieminen MM. No relation between apolipoprotein E alleles and obstructive sleep
apnea.
Clin Genet.1998;53:147-148.Google Scholar 41.Redline S, Tishler PV, Strohl KP. The genetics of the obstructive sleep apnea hypopnea syndrome. In: Pack AI, ed. Sleep Apnea. Pathogenesis, Diagnosis,
and Treatment. New York, NY: Marcel Dekker; 2002:235-264.