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Nieto FJ, Young TB, Lind BK, et al. Association of Sleep-Disordered Breathing, Sleep Apnea, and Hypertension in a Large Community-Based Study. JAMA. 2000;283(14):1829–1836. doi:10.1001/jama.283.14.1829
Author Affiliations: Departments of Epidemiology, Johns Hopkins School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, Md (Drs Nieto and Samet); Preventive Medicine, University of Wisconsin, Madison (Dr Young); Biostatistics, University of Washington, Seattle (Ms Lind); Epidemiology, University of Minnesota, Minneapolis (Dr Shahar); Division of Clinical Epidemiology, University Hospital-Rainbow Babies & Children's Hospital, Cleveland, Ohio (Dr Redline); Mathematics Department, Boston University, Boston, Mass (Dr D'Agostino); Division of Geriatric Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pa (Dr Newman); Respiratory Sciences Center, University of Arizona College of Medicine, Tucson (Dr Lebowitz); and Cardiovascular Center, New York-Cornell Medical Center, Cornell University, New York, NY (Dr Pickering).
Context Sleep-disordered breathing (SDB) and sleep apnea have been linked to
hypertension in previous studies, but most of these studies used surrogate
information to define SDB (eg, snoring) and were based on small clinic populations,
Objective To assess the association between SDB and hypertension in a large cohort
of middle-aged and older persons.
Design and Setting Cross-sectional analyses of participants in the Sleep Heart Health Study,
a community-based multicenter study conducted between November 1995 and January
Participants A total of 6132 subjects recruited from ongoing population-based studies
(aged ≥40 years; 52.8% female).
Main Outcome Measures Apnea-hypopnea index (AHI, the average number of apneas plus hypopneas
per hour of sleep, with apnea defined as a cessation of airflow and
hypopnea defined as a ≥30% reduction in airflow
or thoracoabdominal excursion both of which are accompanied by a ≥4% drop in oxyhemoglobin
saturation), obtained by unattended home polysomnography. Other measures include
arousal index; percentage of sleep time below 90% oxygen saturation; history
of snoring; and presence of hypertension, defined as resting blood pressure
of at least 140/90 mm Hg or use of antihypertensive medication.
Results Mean systolic and diastolic blood pressure and prevalence of hypertension
increased significantly with increasing SDB measures, although some of this
association was explained by body mass index (BMI). After adjusting for demographics
and anthropometric variables (including BMI, neck circumference, and waist-to-hip
ratio), as well as for alcohol intake and smoking, the odds ratio for hypertension,
comparing the highest category of AHI (≥30 per hour) with the lowest category
(<1.5 per hour), was 1.37 (95% confidence interval [CI], 1.03-1.83; P for trend=.005). The corresponding estimate comparing
the highest and lowest categories of percentage of sleep time below 90% oxygen
saturation (≥12% vs <0.05%) was 1.46 (95% CI, 1.12-1.88; P for trend <.001). In stratified analyses, associations of hypertension
with either measure of SDB were seen in both sexes, older and younger ages,
all ethnic groups, and among normal-weight and overweight individuals. Weaker
and nonsignificant associations were observed for the arousal index or self-reported
history of habitual snoring.
Conclusion Our findings from the largest cross-sectional study to date indicate
that SDB is associated with systemic hypertension in middle-aged and older
individuals of different sexes and ethnic backgrounds.
Sleep-disordered breathing (SDB) and the related clinical syndrome,
sleep apnea, have been associated with hypertension in clinical reports since
the early 1980s.1-4
Earlier studies of this association used self-reported history of "snoring"
as a surrogate for the presence of sleep apnea. Although some of these studies
showed an independent association between snoring and hypertension,5-7 others found that this
relationship may be explained by confounding effects of age, sex, or obesity.8-11 Two
recent studies have demonstrated that self-reported history of snoring is
associated with increased incidence of self-reported hypertension in middle-aged
men12 and women.13
Other studies have used polysomnography (PSG), a more objective measure of
SDB. Most of these studies,14-19
but not all,20,21 found an association
between sleep apnea and hypertension, independent of age, sex, body weight,
and other potential confounders. With the exception of the reports from the
Wisconsin Sleep Cohort Study of middle-aged employed persons,15,18
most previous studies were based on a small number of patients in clinical
Given the strong association between SDB and obesity and adiposity measures,23 some researchers have cautioned that even in studies
controlling for body mass index (BMI), there is a potential for residual confounding,
since fat distribution may be the strongest confounding component of obesity.24
This study is based on baseline cross-sectional data from the Sleep
Heart Health Study (SHHS), a multicenter study of the cardiovascular consequences
of sleep apnea in participants recruited from ongoing population-based cohort
studies.25 Our results represent the largest
cross-sectional study to date of the association between SDB and hypertension
in apparently healthy middle-aged and older adults. We assessed SDB in the
subjects' homes using a portable PSG monitor. Its association with blood pressure
and hypertension is examined while controlling for the potential confounding
effects of demographic variables, body weight, and measures of body fat distribution.
The specific aims and design of the SHHS have been previously reported25,26 (also available at: http://www.jhsph.edu/shhs). In brief, SHHS subjects were recruited from participants in ongoing
cohort studies of cardiovascular or respiratory disease. From these parent
cohorts, a sample meeting inclusion criteria (aged 40 years or older; no history
of treatment of sleep apnea with continuous positive airway pressure; no tracheostomy;
no current home oxygen therapy) were invited to participate in the initial
examination of the SHHS. Selection and recruitment procedures varied by study
site according to logistical considerations and participants' characteristics.
Some were recruited at the time of their periodic parent study's clinic examination;
others were recruited by mail or by telephone.25,26
Since the sampling frame for the study already constitutes a somewhat selected
group investigators made no effort to obtain a random sample. To optimize
statistical power by increasing the prevalence of sleep-disordered breathing
in the younger participants, an attempt to oversample persons with a history
of snoring was made in sites recruiting individuals younger than 65 years.
Among 11,053 participants in the parent cohorts identified as potentially
eligible, 3394 (30.7%) refused to participate in the SHHS, while 818 (7.4%)
could not be located or were unable to participate due to illness. The remaining
6841 participants (62%) were enrolled for the SHHS baseline sleep study conducted
in the homes of the participants between November 1995 and January 1998.
A self-administered sleep habits questionnaire on snoring history, sleep
apnea awareness and treatment, and sleepiness was administered prior to the
baseline home visit. The home visit included a brief health interview, assessment
of current medication use,27 blood pressure
and anthropometric measurements, and a full unattended PSG.
Resting blood pressure was measured in the right arm after a 5-minute
rest using a conventional mercury sphygmomanometer. The first and last Korotkoff
sounds were used to determine systolic and diastolic blood pressure, respectively.
The average of the second and third of 3 consecutive measurements was used
as the blood pressure value in this report. Weight was measured in light clothes
on a portable scale. Neck circumference was measured just below the laryngeal
prominence using standard methods.28
Polysomnography was conducted using a Compumedics PS-2 system (Compumedics
Pty Ltd, Abbotsford, Australia) with the following montage: central electroencephalogram,
electrooculogram, chin electromyogram, single bipolar electrocardiogram, finger
pulse oximetry, chest and abdominal excursion by respiratory inductance plethysmography,
and airflow by oronasal thermocouples, body position, and ambient light.29 Sensors were placed and equipment calibrated during
the evening home visit by centrally trained and certified technicians.26 Data were stored in real time on PCMCIA cards. The
equipment was retrieved the next morning.
Of the 6841 home PSG studies attempted, 401 (5.9%) failed (sensor losses
or <4 hours of recorded data of sufficient quality), leaving 6440 PSG studies
scored at the SHHS Reading Center (Case Western Reserve University, Cleveland,
Ohio). Detailed protocol for central scoring of sleep stages, arousals, and
respiratory events has been described.29
Following standard recommendations,30,31
hypertension was defined as blood pressure of at least 140/90 mm Hg, or current
treatment with antihypertensive medications.
Sleep-disordered breathing was assessed using the
apnea-hypopnea index (AHI), defined as the average number of apneic plus hypopneic episodes per
hour of sleep. Apnea was defined as a complete or an almost complete cessation
of airflow and hypopnea as a decrease in airflow or thoracoabdominal excursion
of at least 30% of baseline for 10 seconds or more, accompanied by a 4% or more.
Both apnea and hypopnea must be accompanied by a 4% or more decrease in oxygen saturation.
Other indices of SDB included arousal
index (average number of arousals per hour of sleep, with arousals identified
following modified American Sleep Disorders Association criteria),32 percentage of sleep time with oxygen saturation below
90%, and snoring history (self-report of snoring ≥3 nights a week). The
AHI and the arousal index scoring reliability have been reported.33 The AHI as defined herein showed high interscorer
reliability (intraclass correlation coefficient [ICC], 0.99), whereas the
reliability of scoring for the arousal index was moderate (ICC range, 0.54-0.72)
depending on the quality of sleep recording and experience of the scorer.
Smoking history was obtained from the health interview questionnaire.
Usual alcohol intake, waist and hip circumferences, and height were obtained
from the parent studies. Body mass index was calculated as weight in kilograms
divided by the square of height in meters. Overweight and obesity categories
were defined according to current recommendations (Table 1).34
Among the 6440 SHHS participants, this report is based on 6132 individuals
with complete information on demographics, weight, height, hypertension status,
and AHI. For arousal index, analyses were restricted to the 5112 participants
with electroencephalogram and electromyogram data of sufficient quality for
Means and proportions were compared using analysis of variance and χ2 tests, respectively. The relationship between the SDB variables and
blood pressure values was analyzed using multiple linear regression. Due to
the skewed distribution of SDB measures and to prevent undue influence of
observations with extreme values, these variables were log-transformed (natural
Logistic regression was used to calculate the odds ratio (OR) of hypertension
comparing SDB categories, while adjusting for possible confounders.35 In these analyses, AHI was categorized according
to the observed distribution as well as by commonly used clinical cutoff points.
The reference category was an AHI of less than 1.5 per hour (approximately
the bottom quartile of the AHI distribution); other cutoff values were AHIs
equal to 5, 15, and 30 per hour, resulting in 5 categories. Criteria for the
categorical definitions of the other SDB variables were chosen so that the
categories would divide the study population into groups of approximately
the same size as for the AHI criteria.
To assess the impact of using arbitrary cutoff points, the OR of hypertension
in relationship to SDB variables was also estimated using nonparametric logistic
regression.36,37 With this method,
the dose-response relationship between a putative risk factor (eg, AHI) and
an outcome (eg, hypertension) can be estimated without parametric assumptions
about the risk trend and without the need to categorize the continuous exposure
variable. As a result of the high prevalence of hypertension, the ORs presented
herein should not be interpreted as risk ratios.38
Nonparametric logistic regression used the generalized additive models,
as implemented by the GAM function in S-PLUS software.39
All other statistical analyses were conducted using SAS software (SAS Institute,
Descriptive characteristics of the study population are presented in Table 1 and Table 2, both overall and by AHI categories. Compared with those
with lower AHI values, participants with higher levels of SDB (AHI ≥15
per hour) included a larger proportion of men, persons aged 65 years or older,
American Indians, former smokers, and being obese (Table 1). Neck circumference and waist-to-hip ratio were also significantly
higher in participants with high AHI values. The results shown in Table 1 should be interpreted with caution
because they are unadjusted. With regard to ethnicity, for example, the increased
prevalence of SDB among American Indians seems to be entirely explained by
differences in age and the higher prevalence of obesity in this ethnic group.
The unadjusted mean AHI for each ethnic group was 8.7 for whites; 9.0 for
blacks; 10.7 for American Indians; and 7.2 for others (P<.001); after adjusting for age and BMI, these values were 8.8,
8.3, 8.7, and 8.9, respectively (P=.81).
As expected, AHI was strongly associated with self-reported history
of snoring as well as with other measures of SDB, including arousal index
and sleep time below 90% oxygen saturation (Table 2).
Excluding participants taking antihypertensive medications, AHI was
linearly associated with blood pressure values (Table 2). However, multiple linear regression analyses (Table 3) showed that these associations
were partially explained by BMI, although a statistically significant linear
association between AHI and blood pressure persisted even after adjustment
for BMI. Arousal index was also associated with blood pressure, while the
associations between sleep time below 90% oxygen saturation and both systolic
and diastolic blood pressure became nonsignificant after BMI adjustment (Table 3).
To use data from the entire cohort, including those in treatment, the
remaining analyses were based on hypertension status. In unadjusted analyses,
the prevalence rates of hypertension in increasing AHI categories were 43%
(<1.5 per hour), 53% (1.5-4.9 per hour), 59% (5-14.9 per hour), 62% (15-29.9
per hour), and 67% (≥30 per hour). Adjustment for age and demographic characteristics
showed that the odds of hypertension increased with escalating AHI categories
in a graded-dose response fashion (Table
4); the OR of hypertension comparing participants with high AHI
(≥30 per hour) to participants in the lowest AHI category (<1.5 per
hour) was 2.27 (95% confidence interval [CI], 1.76-2.92). Similarly strong
associations were present for sleep time below 90% oxygen saturation, whereas
weaker associations were seen for the arousal index and snoring. Adjustment
for BMI reduced these estimates, although statistically significant elevated
ORs were still present for the highest categories of AHI and sleep time below
90% oxygen saturation (OR range, 1.5-1.6). Further adjustment for other anthropometric
measurements (neck circumference and waist-to-hip ratio) reduced these estimates
only slightly. The latter model showed no significant associations with arousal
Adding cigarette smoking and alcohol intake to the model had very little
impact on the estimates (slightly reducing and slightly increasing the point
estimates for AHI and sleep time below 90% oxygen saturation, respectively).
The associations between the top and bottom categories of AHI, sleep
time below 90% oxygen saturation, and snoring with hypertension in subgroups
defined according to demographic variables and body weight categories are
shown in Table 5. Associations
between these SDB measures and hypertension were present in all subgroups,
although estimates vary in part due to small sample sizes in these subgroup
analyses. The associations between AHI and hypertension among women were markedly
weakened in the fully adjusted model (including BMI, neck circumference, and
waist-to-hip ratio); on the other hand, the association between sleep time
below 90% oxygen saturation and hypertension was still present after full
adjustment in both women and men (OR, 1.3). Similarly, although the association
between AHI and hypertension seemed stronger among those who were younger
than 65 years, the opposite was observed for sleep time below 90% oxygen saturation
after full adjustment. Associations for either SDB measure were present in
all 3 major ethnic groups in the SHHS cohort (white, black, American Indians),
although some of the CIs overlap 1 due to relatively small sample sizes. Likewise,
associations were present in all 3 relative weight categories in this population,
although slightly stronger in the high-body weight groups.
As shown in Figure 1, nonparametric
logistic regression analyses confirmed the dose-response relationship between
AHI or sleep time below 90% oxygen saturation and the odds of hypertension.
The data suggest that, although with some fluctuations probably stemming from
random variability, the adjusted odds of hypertension increase steadily with
AHI from values of about 15 or 20 per hour, reaching ORs greater than 2 for
the very high AHI values. For sleep time below 90% oxygen saturation, a steady
increase in the fully adjusted OR is only observed for values higher than
This large cross-sectional study in healthy middle-aged and older adults
shows that SDB is associated with prevalent hypertension. After controlling
for the main potential confounders (age, sex, BMI, and other measures of adiposity),
as well as for other potentially relevant variables (alcohol intake, smoking),
high levels of AHI or sleep time below 90% oxygen saturation were associated
with greater odds of hypertension in a dose-response fashion (Table 4 and Figure 1).
In contrast, with these objective measures of SDB, self-reported snoring showed
little or no association with hypertension. This contrast may be due to misclassification
errors resulting from the limited validity of self-reported snoring information40,41 and could explain the inconsistencies
in previous studies that use snoring as an indicator of SDB.5-10
The weak associations for arousal index and AHI may be explained by the dilution
effect, which stems from the exclusion of 1011 participants from the arousal
index analyses because of unreliable electroencephalogram and electrocardiogram
data. This group may include a large proportion of persons with SDB who because
of movement associated with poor sleep quality may be more prone to sensor
loss than those who do not have SDB. The median AHI of 4.2 per hour among
those with and 5.3 per hour among those without arousal index data supports
the latter hypothesis.
The large sample size allowed us to conduct analyses stratified according
to potential effect modifiers (Table 5).
Studies have reported stronger association between SDB and hypertension among
younger persons than older persons,8,42
an effect that is apparent for AHI but not for sleep time below 90% oxygen
saturation in our study. Associations between SDB measures and hypertension
were seen in both sexes and in all ethnic groups and were slightly stronger
among those who were overweight or obese.
Unlike previous studies that did not adequately control for potential
we attempted to account for all of the most important potential confounders,
including measures of overweight and fat distribution. As expected, controlling
for BMI diminished the strength of the association between estimates of SDB
and hypertension (Table 3 and Table 4). Additionally, controlling for
neck circumference and waist-to-hip ratio changed these estimates only slightly,
thus providing little support to criticisms that previous studies that only
controlled for BMI alone may have been subject to to residual confounding
due to obesity.24
Our results support the common conceptualization of overweight as a
possible confounder of the putative association between SDB and hypertension.
However, these observations are also consistent with an alternative model,
whereby sleep apnea is one of the intermediary mechanisms by which overweight
is causally related to hypertension. Under this alternative model, the BMI-adjusted
estimates presented in Table 4, Table 5, and Figure 1 may be subject to overadjustment. Indeed, since AHI is
measured with error because of night-to-night variability,24
adjusting for BMI, which is more precisely measured and is strongly correlated
with the true level of SDB, may be equivalent to adjusting for this true underlying
level of the very condition that the observed AHI is trying to measure. Thus,
the true level of association between SDB and hypertension may lie between
the unadjusted estimates (subject to confounding) and the BMI-adjusted estimates
(partially subject to overadjustment).
Nonetheless, given the observational nature of this study, the possibility
of residual confounding due to unmeasured or unknown confounders cannot be
ruled out. Moreover, if confounding variables were measured with error, adjustment
may be incomplete leading to a residual overestimate of the associations.44(p328) Potential problems derived from the somewhat
arbitrary AHI definition (highly dependent on the definition used for hypopnea
identification)45 were addressed by using alternative
definitions of SDB in addition to AHI (Table 4 and Table 5)
and by using nonparametric logistic regression analyses that are not dependent
on arbitrary cutoff points (Figure 1).
The possibility of selection biases due to the volunteer character of
the sample (participants in ongoing cohort studies who agreed to undertake
home PSG) needs to be considered as well. However, we consider it unlikely
that these biases will affect the internal validity of the main results (ie,
the association between SDB and hypertension), particularly because of the
internal consistency in stratified analyses (Table 5). In any event, we consider that this study will be less
prone to these biases than previous studies in patient populations.
Finally, the cross-sectional nature of the study precludes definitive
causal inferences. The temporal relationship between SDB and hypertension
cannot be firmly established. In addition, prevalence-incidence (duration)
bias may also be affecting these cross-sectional results. If sleep apnea is
related to increased mortality, as suggested in previous studies,46-48 the survival of a
hypertensive person with sleep apnea would tend to be shorter than that of
a hypertensive person without sleep apnea. Under these assumptions, the cross-sectional
estimates from this and similar studies will tend to underestimate the true
The hypothesis of a causal association between sleep apnea and hypertension
is supported by evidence from intervention trials, showing that successful
treatment of sleep apnea by means other than weight loss (eg, continuous positive
airway pressure) is accompanied by significant decreases in both daytime and
nighttime blood pressure.50-52
The mechanisms underlying the association between SDB and hypertension are
not entirely clear. Several have been proposed,10,52-54
including hemodynamic disturbances resulting from intermittent negative intrathoracic
pressure during apneic episodes, recurrent episodes of hypoxemia and hypercapnia
resulting in abnormal activation of arterial chemoreceptors and increased
sympathetic activity, and increased sympathetic activity associated with repeated
arousals during sleep.
In summary, this study suggests an independent association between sleep
apnea and hypertension, particularly among the middle-aged participants. However,
because of the observational and cross-sectional nature of the study, these
results should be interpreted with caution. Further prospective studies on
the longitudinal association between SDB in relationship to changes in blood
pressure and hypertension incidence will help elucidate the true nature and
magnitude of the association.
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