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Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal Study of Moderate Weight Change and Sleep-Disordered Breathing. JAMA. 2000;284(23):3015–3021. doi:10.1001/jama.284.23.3015
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
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
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
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.
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
morbidity. Recently, indicators of even mild SDB have been significantly related
to hypertension, cardiovascular disease, and mortality in population-based
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
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
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.
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
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.
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 χ2 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.
At baseline, unadjusted means (SDs) of AHI were 7.4 (13.1) events/h
in obese participants (BMI≥30 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 (AHI2≥15
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.
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.
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 (AHI≥15 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.
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
investigations typically find significant correlations. Five small (n≤15)
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 (n≤55) 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
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
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
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.
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