ORIGINAL CONTRIBUTION

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

S

LEEP - 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 publichealth burden of SDB because of demonstrated cross-sectional and retrospective associations between SDB andbehavioral7-15 and cardiovascular16-23
morbidity. Recently, indicators of evenmild SDB have been significantly related to hypertension, cardiovascular disease, and mortality in populationbased prospective studies.24-27 Althoughnightly use of continuous positive airway pressure can prevent apnea and hypopnea events, this therapy poses toohigh a life-long patient burden to bepractical for mild or asymptomatic SDB.
Thus, risk-factor modification may be
the most feasible way to reduce theprevalence of SDB on a large scale.
Obesity, a strong correlate of SDB, isextremely prevalent in the United Statesand 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 anddisturbance of the relation between respiratory drive and load compensation.31 If obesity is causally related to SDB,
weight loss and the prevention of weightContext 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 weightchange 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 moderateto-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 moderateto-severe SDB.
Conclusions Our data indicate that clinical and public health programs that resultin even modest weight control are likely to be effective in managing SDB and reducing new occurrence of SDB.
www.jama.com

JAMA. 2000;284:3015-3021

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 sleepdisorders clinics32-40 or cross-sectional
population-based samples.3-6,22,41-44 Several small studies, most lacking control
groups, have found marked reductionsin indicators of SDB following surgical45-49 or diet-related weight loss49-58 inobese 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-tosevere SDB.

©2000 American Medical Association. All rights reserved.

To date, there has been no large
population-based study of the longitudinal association of change in weightand SDB. Longitudinal information isespecially crucial in preclinical, asymptomatic people with mild-to-moderate SDB who are most likely to benefit
from noninvasive and preventive weightcontrol strategies. Our longitudinal
study was designed to measure the degree to which weight gain is associated with increased SDB severity andAuthor 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.
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@facstaff.wisc.edu).

(Reprinted) JAMA, December 20, 2000—Vol 284, No. 23

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

3015

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING

Table 1. Summary of Key Variables for Eligible Baseline Participants Invited for a Follow-up
Study and Participants in the Follow-up Study*
InvitedParticipants
Baseline(n = 948)
45 (8)

Baseline(n = 690)
46 (7)

Follow-up(n = 690)
50 (7)

Male, No. (%)

542 (57)385 (56)385 (56)

AHI, events/hour
Mean (SD)Median4.5 (9.8)
1.1

4.1 (9.1)
1.1

5.5 (10.8)
1.6

No. (%),55-,15755 (80)120 (13)554 (80)90 (13)495 (72)127 (18)$15

73 (8)

46 (7)

68 (10)86 (20)85 (19)88 (20)29 (6)

29 (6)

30 (7)

38 (4)
0.89 (0.09)

38 (4)
0.89 (0.09)

38 (4)
0.89 (0.09)

Variable
Age, mean (SD), y

Weight, mean (SD), kg
BMI, mean (SD), kg/m2
Neck girth, mean (SD), cm
Waist girth-to-hip girth ratio, mean (SD)

Follow-up Participants

Skinfold total, mean (SD), mm†

80 (32)81 (32)106 (45)Hypertensive, No. (%)‡
Smoker, No. (%)
Alcohol, mean (SD), drinks/wk

276 (29)181 (19)4 (7)195 (28)120 (17)4 (7)207 (30)112 (16)4 (5)*AHI indicates apnea-hypopnea index; BMI, body mass index.
†Sum of biceps, triceps, subscapular, and suprailiac.
‡Blood pressure $140/90 mm Hg or current use of antihypertensive medications.

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
ParticipantsParticipants 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 thesample 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 ordecompensated 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 physi-

ologic parameters or less than 4 hoursof sleep time (n = 42), medical treatment for SDB (n = 20), or physiciandiagnosed 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 completedbaseline 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.
Data Collection

Protocols and informed consent documents for WSCS were approved by theinstitutional 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

3016 JAMA, December 20, 2000—Vol 284, No. 23 (Reprinted)

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, includingheight 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 toscore 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 airpressure, 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 excur-©2000 American Medical Association. All rights reserved.

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING

sions. Sleep state and respiratory event
scorings were performed by trained
sleep technicians and reviewed by anexpert 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 thesum 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 apneahypopnea 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 tomeasure the relation between weightchange and change in SDB severity. Bothapproaches are detailed below.
Multiple linear regression models
were used to assess the association between change in the AHI and weightchange while controlling for potential
confounding variables. These models
were implemented by regressing the logof the ratio of follow-up AHI divided
by baseline AHI (ie, log e [AHI 2 + 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). Log e ([AHI 2 + 1]/[AHI 1 + 1]), as opposed to othermeasures 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 boththe baseline and follow-up AHI measures was necessary because some participants had an AHI equal to zero. We
refer to this model as the “progres-sion” model, although reductions aswell 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) associatedwith percentage weight change. We
refer to this as the “incidence” model.
Crossing the 15 events/h cutoff ineither direction is accommodated by
the model, allowing the model toaccount for an association of bothweight gain and loss with changing
SDB classification. The conditionalmodel implicitly controls for fixed
intraperson characteristics, such as sex
and genetic profile.
The following were investigated as interacting and confounding factors inlinear regression models, and, when appropriate, in the conditional logistic regression models: sex; baseline values ofage, smoking habits (never, ever, andcurrent-use status and cigarette packs perweek), 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 waistto-hip girth ratio), levels of education andphysical activity; and 4-year change insmoking 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 weightchange were tested for statistical significance. The statistical significance (2tailed 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 x2 statistic.61 Regression diagnostics were performed toassess model fit and adequacy of compliance with modeling assumptions.
Intrasubject variability and measurement error in the AHI prevented mean-©2000 American Medical Association. All rights reserved.

ingful 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 withless 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 andpercentage AHI change appears to be
independent of baseline AHI. Thus, we
expect that the regression model results presented here are valid across therange of baseline AHI values analyzed
in this study.
RESULTSAt 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 weightparticipants (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 inweight (FIGURE). 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 atbaseline, 17 fell below 15 events/h atfollow-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, comparedwith a mean weight increase of 4.0 (6.9)(Reprinted) JAMA, December 20, 2000—Vol 284, No. 23

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

3017

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING

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 insmoking 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 exFigure. Mean Change in the ApneaHypopnea Index AHI by Weight Change
Category

Mean Change in AHI, Events/h

6

4

2

0

–2

–4

–20% to< –10%(n = 22)–10% to< –5%(n = 39)–5% to< +5%(n = 371)+5% to< +10%(n = 179)+10% to+20%(n = 79)

Change in Body Weight

Table 2. Estimated Percent Change in the
AHI for Selected Decrements and Incrementsof Percent Body Weight*
Percent Change
Estimated Percent
in Weight
Change in AHI(vs No Change) (95% Confidence Interval)†−20−48 (−58 to −35)
−10−26 (−34 to −18)−5
−14 (−18 to −9)+5+15 (+10 to +21)
+10+32 (+20 to +45)
+20
+70 (+42 to +104)*AHI indicates apnea-hypopnea index. Adjusted for sex,change in cigarette packs/wk, baseline body massindex (kg/m2), and baseline age. All P,.001.
†In addition to change expected if weight remained stable.

ample, 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, alcoholuse, 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 weightgain in this study, and smoking was positively related to increased SDB severity
in a previous cross-sectional analysis
from the WSCS.62 Baseline values andchanges in skinfold thicknesses; neck,waist, and hip girths; and waist-to-hip
girth ratio were not significant predictors of change in the AHI independentof 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 weightchange. 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, in-Table 3. Conditional Logistic Regression Coefficients and Odds Ratios* for Developmentof Moderate-to-Severe Sleep-Disordered Breathing (SDB) (AHI$15 Events/h) for Selected
Increments of Weight Gain
Percent Gain in Weight(vs No Gain)

Regression
Coefficient (SE)

5
10
20

0.9 (0.3)
1.8 (0.5)
3.6 (1.1)

Estimated Odds Ratio
for Moderate-to-Severe SDB(95% Confidence Interval)
2.5 (1.5 to 4.1)
6.0 (2.2 to 17.0)
36.6 (4.6 to .50)

*Adjusted for change in cigarette packs/wk. AHI indicates apnea-hypopnea index. All P,.001.
3018 JAMA, December 20, 2000—Vol 284, No. 23 (Reprinted)

cluding baseline weight, were statistically significant.
Conditional logistic regression wasused 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 bodyweight. For persons with AHI greaterthan 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 assex, are implicitly accounted for. There
were no significant interactions between weight change and examined covariates.
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 foundweight gain predicted the developmentof moderate-to-severe SDB. Weight loss
was associated with a reduced SDB severity and likelihood of developing SDB.
These results were independent of manypotential confounding factors, such asage, baseline body habitus measures, andchange in smoking habits.
This prospective study benefited
from a unique combination of features. It used a large population-based
sample that provided more precise andgeneralizable results than previous
clinic-based studies of weight loss andsevere SDB in patients who were morbidly obese. Unlike those studies, thisstudy was able to assess the relation between weight gain and SDB. This is an
important advantage for public healthinterpretation of the study because of
the increasing prevalence of obesity in©2000 American Medical Association. All rights reserved.

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING

the United States.28-30 This study also
benefits from high-quality laboratorybased polysomnographic assessment ofSDB, currently the diagnostic gold standard for SDB.
Our results are largely consistent
with other research examining excess
weight and its relation to SDB. Crosssectional clinic-32-40 and populationbased3-6,22,41-44 investigations typicallyfind significant correlations. Five small(n#15) uncontrolled studies of surgical weight loss45-49 in patients who wereseverely 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 decreasesof 47% and 61%, respectively. Twosmall (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, ourlongitudinal results, those from crosssectional and weight-loss studies byother investigators, and biological plausibility provide evidence consistent witha causal link between excess bodyweight 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 beencross-sectionally associated with SDB.
Accordingly, we investigated changes inneck 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 predictedchanges in AHI as well as those othermeasures and that our models were notsubstantially improved by the additionof other body habitus parameters. We
chose to focus on weight change as themeasure 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 thebaseline sample (258/948 baseline
participants) either refused or were
not reachable for follow-up. If therelation between body weight and
SDB is substantially different in theentire 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, invitedbaseline sample is 2.1 (0.1). Here wefind no substantial difference in therelation of SDB and weight in thesamples. Although this does not ruleout 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 inthe sleep cohort experienced large percentage changes in weight, we do notrecommend generalizing our findings
to very large weight changes. Supplementary semiparametric spline modeling of our data indicated that within
the range of ±20% weight change, theassociation of weight change and SDBchange (as characterized by our progression model) was well described bya linear function. However, the relation at greater weight change plateaued. Unfortunately, the number ofparticipants with extreme weightchange proved too few to carefully characterize associations that involved more©2000 American Medical Association. All rights reserved.

than 20% weight change. Thus, theseparticipants were excluded, and thefindings presented in this report should
not be extrapolated beyond 20% weightchange.
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 otherdisturbance. Our progression model
does not readily accommodate such
persons.
Third, there is a substantial amountof variability in AHI change that is notaccounted 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 weightparticipants, the mean AHI was low andweight loss uncommon. Thus, we could
not rigorously address the associationof weight loss and reduced SDB severity in the normal weight participants
with SDB.
Fifth, it is plausible that excess bodyweight 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 theproximity of the upper airway. With ourstudy design, we were unable to determine to what extent one or both of theseprocesses might be occurring. However, we found change in weight andbaseline habitus (BMI in our progression model) independently predictedchange in SDB severity, indicating thatboth an accelerated progression andshort-term response might occur.
Weight-loss studies that have demonstrated a reduction in SDB severity havetended to be short term, also suggesting that at least some of the responseof SDB to excess weight is incurred almost simultaneously with weightchange.

(Reprinted) JAMA, December 20, 2000—Vol 284, No. 23

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

3019

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING

Finally, we do not know the causesof 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 ofdiet, exercise, and other related medical and lifestyle factors.
Obesity is a growing worldwide healthproblem, and its strong association with
SDB is likely to be causal. It follows thatthe incidence of SDB will continue togrow in prominence and that clinical andpublic health strategies using weightcontrol will be attractive approaches tothe treatment of SDB. Our findings haveimportant 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, overweightpeople 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.
Funding/Support: This research was supported by
grants R01HL62252, RR03186, and AG14124-05 fromthe National Institutes of Health.
Acknowledgment: Polysomnographic evaluation was
conducted by the Sleep Laboratory Core of the University of Wisconsin Specialized Center of Researchin Cardiopulmonary Disorders of Sleep. We thankSteven Weber, PhD, Laurel Finn, Tony Jacques, Linda
Evans, and Kathy Pluff for their technical expertise.
REFERENCES
1. Kripke DF, Ancoli-Israel S, Klauber MR, Wingard
DL, Mason WJ, Mullaney DJ. Prevalence of sleepdisordered breathing in ages 40-64 years: a populationbased survey. Sleep. 1997;20:65-76.
2. Bearpark H, Elliott L, Grunstein R, et al. Snoring andsleep apnea: a population study in Australian men. Am
J Respir Crit Care Med. 1995;151:1459-1465.
3. Olson LG, King MT, Hensley MJ, Saunders NA. A
community study of snoring and sleep-disorderedbreathing: prevalence. Am J Respir Crit Care Med.
1995;152:711-716.

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.
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.
6. Stradling JR, Crosby JH. Predictors and prevalence of obstructive sleep apnoea and snoring in 1001
middle aged men. Thorax. 1991;46:85-90.
7. Teran-Santos J, Jimenez-Gomez A, CorderoGuevara J, for the Cooperative Group BurgosSantander. The association between sleep apnea andthe risk of traffic accidents. N Engl J Med. 1999;340:
847-851.
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 CareMed. 1999;159:502-507.
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.
10. Redline S, Strauss M, Adams N, et al. Neuropsychological function in mild sleep apnea. Sleep. 1997;
20:160-167.
11. Young T, Blustein J, Finn L, Palta M. Sleepdisordered breathing and motor-vehicle accidents ina population-based sample of employed adults. Sleep.
1997;20:608-613.
12. Kim HC, Young T, Matthews CG, Weber SM,
Woodard AR, Palta M. Sleep-disordered breathing andneuropsychological deficits. Am J Respir Crit Care Med.
1997;157:1813-1819.
13. Finn L, Young T, Palta M, Fryback D. Sleepdisordered breathing and self-reported general healthstatus in the Wisconsin Sleep Cohort Study. Sleep.
1998;21:701-706.
14. Guilleminault C, Stoohs R, Kim Y, Chervin R, BlackJ, Clerk A. Upper airway sleep-disordered breathing
in women. Ann Intern Med. 1995;122:493-501.
15. Johns MW. Daytime sleepiness, snoring and obstructive sleep apnea. Chest. 1993;103:30-36.
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.
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.
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.
19. Nieto FJ, Young TB, Lind BK, et al, for the Sleep
Heart Health Study. Association of sleep-disorderedbreathing, sleep apnea, and hypertension in a large
community-based study. JAMA. 2000;283:18291836.
20. Partinen M, Jamieson A, Guilleminault C. Longterm outcome for obstructive sleep apnea syndrome
patients: mortality. Chest. 1988;94:1200-1204.
21. Partinen M, Guilleminault C. Daytime sleepinessand vascular morbidity at seven-year follow-up in obstructive sleep apnea patients. Chest. 1990;97:2732.
22. Schmidt-Nowara WW, Coultas DB, Wiggins C,
Skipper BE, Samet JM. Snoring in a HispanicAmerican population: risk factors and association withhypertension and other morbidity. Arch Intern Med.
1990;150:597-601.
23. Young T, Peppard P, Palta M, et al. Populationbased study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med. 1997;157:
1746-1752.

3020 JAMA, December 20, 2000—Vol 284, No. 23 (Reprinted)

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.
25. Hu FB, Willett WC, Manson JE, et al. Snoring andrisk of cardiovascular disease in women. J Am Coll Cardiol. 2000;35:308-313.
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.
27. Peppard PE, Young TB, Palta M, Skatrud J. Prospective study of the association between sleepdisordered breathing and hypertension. N Engl J Med.
2000;342:1378-1384.
28. Lewis CE, Jacobs DR Jr, McCreath H, et al. Weightgain 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.
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.
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.
31. Strobel RJ, Rosen RC. Obesity and weight loss inobstructive sleep apnea: a critical review. Sleep. 1996;
19:104-115.
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.
33. Katz I, Stradling J, Slutsky AS, Zamel N, Hoffstein
V. Do patients with obstructive sleep apnea have thicknecks? Am Rev Respir Dis. 1990;141:1228-1231.
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.
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.
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.
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.
38. Shelton KE, Woodson H, Gay S, Suratt PM. Pharyngeal fat in obstructive sleep apnea. Am Rev Respir
Dis. 1993;148:462-466.
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.
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.
41. Jennum P, Hein HO, Suadicani P, Gyntelberg F. Cardiovascular risk factors in snorers: a cross-sectional studyof 3,323 men aged 54 to 74 years: the Copenhagenmale study. Chest. 1992;102:1371-1376.
42. Jennum P, Sjol A. Snoring, sleep apnoea and cardiovascular risk factors: the MONICA II Study. Int J
Epidemiol. 1993;22:439-444.
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.

©2000 American Medical Association. All rights reserved.

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/2022

MODERATE WEIGHT CHANGE AND SLEEP-DISORDERED BREATHING
45. Harman EM, Wynne JW, Block AJ. The effect ofweight loss on sleep-disordered breathing and oxygen desaturation in morbidly obese men. Chest. 1982;
82:291-294.
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:
125-129.
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.
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.
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.
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;138:11921195.
51. Pasquali R, Colella P, Cirignotta F, et al. Treatment of obese patients with obstructive sleep apnea
syndrome (OSAS): effect of weight loss and interfer-

ence of otorhinolaryngoiatric pathology. Int J Obes.
1990;14:207-217.
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.
54. Nahmias J, Kirschner M, Karetzky MS. Weight loss
and OSA and pulmonary function in obesity. N J Med.
1993;90:48-53.
55. Noseda A, Kempenaers C, Kerkhofs M, Houben
JJ, Linkowski P. Sleep apnea after 1 year domiciliary nasalcontinuous positive airway pressure and attempted
weight reduction: potential for weaning from continuous positive airway pressure. Chest. 1996;109:138-143.
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.
57. Schwartz AR, Gold AR, Schubert N, et al. Effect ofweight loss on upper airway collapsibility in obstructive
sleep apnea. Am Rev Respir Dis. 1991;144:494-498.
58. Suratt PM, McTier RF, Findley LJ, Pohl SL, Wilhoit
SC. Changes in breathing and the pharynx after weightloss in obstructive sleep apnea. Chest. 1987;92:631637.

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, Kales A, eds. 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.
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.
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.
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.

Besides learning to see, there is another art to belearned—not to see what is not.
—Maria Mitchell (1818-1889)©2000 American Medical Association. All rights reserved.

(Reprinted) JAMA, December 20, 2000—Vol 284, No. 23

Downloaded From: https://jamanetwork.com/ by a Gakko Hojin Jikei Daigaku User on 08/02/20223021