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Volume 14 No. 09
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Accepted Papers

Scientific Investigations

Prevalence of Obstructive Sleep Apnea and Obesity Among Middle-Aged Women: Implications for Exercise Capacity

Martinha Millianny Barros de Carvalho, RpT1; Ricardo Quental Coutinho, MD2; Isly Maria L. Barros, MD, PhD2; Laura O.B.F. Costa, MD, PhD2; Ana Kelley L. Medeiros, RN1; Thais C. Lustosa, RpT1; Carolina A. Medeiros, RN1; Marcus Vinícius França, RpT1; Tarcya L.G. Couto, RpT1; Ulisses R. Montarroyos, PhD3; Virend K. Somers, MD, PhD4; Rodrigo Pinto Pedrosa, MD, PhD1,5
1Sleep and Heart Laboratory, Pronto Socorro Cardiológico de Pernambuco (PROCAPE) da Universidade de Pernambuco, Brasil; 2Pronto Socorro Cardiológico de Pernambuco (PROCAPE) da Universidade de Pernambuco, Recife, Brasil; 3Institute of Biological Sciences (ICB) da Universidade de Pernambuco, Recife, Pernambuco, Brasil; 4Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota; 5Hospital Memorial São José - Rede D′Or São Luiz, Pernambuco, Brasil


Study Objectives:

The aim of the current study was to evaluate the association between obstructive sleep apnea (OSA) and exercise capacity in middle-aged women.


Consecutive middle-aged female subjects without cardiovascular disease, aged 45 to 65 years, from two gynecological clinics underwent detailed clinical evaluation, portable sleep study, and treadmill exercise test.


We studied 232 women (age: 55.6 ± 5.2 years; body mass index [BMI]: 28.0 ± 4.8 kg/m2). OSA (apnea-hypopnea index ≥ 5 events/h) was diagnosed in 90 (39%) and obesity (BMI > 30 kg/m2) in 76 (33%) women, respectively. Participants with OSA were older, had a higher BMI, and an increased frequency of arterial hypertension compared to women without OSA. Multiple logistic regression models were used to evaluate the association between OSA and exercise capacity, controlling for traditional risk factors including BMI, age, hypertension, diabetes, and sedentary lifestyle. In multivariate analysis, the presence of obesity without OSA was associated with low exercise capacity (odds ratio [OR] 2.88, 95% confidence interval [CI] 1.02–8.11, P = .045), whereas the presence of OSA without obesity was not (OR 1.07, 95% CI 0.31–3.69, P = .912). However, the coexistence of obesity and OSA increased markedly the odds of reduction in exercise capacity (OR 9.40, CI 3.79–23.3, P < .001).


Obesity and OSA are common conditions in middle-aged women and may interact to reduce exercise capacity. These results highlight the importance of obesity control programs among women, as well as the diagnosis of comorbid OSA in older women.


de Carvalho MM, Coutinho RQ, Barros IM, Costa LO, Medeiros AK, Lustosa TC, Medeiros CA, França MV, Couto TL, Montarroyos UR, Somers VK, Pedrosa RP. Prevalence of obstructive sleep apnea and obesity among middle-aged women: implications for exercise capacity. J Clin Sleep Med. 2018;14(9):1471–1475.


Current Knowledge/Study Rationale: Obstructive sleep apnea (OSA) and obesity may both impair exercise capacity, which is an important cardiovascular risk predictor.

Study Impact: Our study showed an interaction between obesity and OSA in exercise functional capacity, suggesting that OSA and obesity may together contribute to reduce functional capacity, resulting in heightened cardiovascular risk in middle-aged women.


Metabolic and hormonal changes during the aging process in women modify body composition, predisposing to adipose tissue accumulation and obesity, which is a strong risk factor for obstructive sleep apnea (OSA).1 OSA is very common among adults, and affects 17% to 24% of women in the general population.1,2 Among perimenopausal women, each additional year in menopause was associated with a 4% increase in the apneahypopnea index (AHI).3

The reduction of airflow caused by OSA leads to hypoxemia and arousals, with consequent activation of peripheral chemoreceptors, eliciting increased sympathetic outflow both during sleep and wakefulness.4 This sympathetic hyperactivity has been proposed as a contributor to impaired hemodynamic responses and a decrease in exercise capacity.5

Exercise capacity is an important predictor of cardiovascular risk, varying according to age, sex, and body mass index (BMI).6 Furthermore, exercise capacity is an independent predictor of death in asymptomatic women.7 Because there is frequent overlap between OSA and obesity, it has been suggested that the low exercise capacity in OSA might be explained by obesity itself.8 In the current study, we evaluated the association between OSA and low exercise capacity in middle-aged women.


Population and Protocol

A total of 232 consecutive perimenopausal or postmenopausal women (with menstrual irregularity > 60 days), aged 45 to 65 years, were enrolled from two primary care gynecological clinics from May 2011 to June 2012. Exclusion criteria included a history of smoking in the past 5 years, hormone replacement therapy, symptoms of heart failure, history of coronary artery disease, atrial fibrillation or stroke, and renal failure. At least three resting blood pressure (BP) measurements were taken at 1-minute intervals, and the average of the two final measurements was considered as the individual's BP. Those with systolic BP over 140 mmHg and diastolic BP over 90 mmHg or those on antihypertensive drugs were considered hypertensive.9 Diabetes was defined as a fasting glucose > 125 mg/dL or the use of hypoglycemic drugs.10 Dyslipidemia was diagnosed according to the National Cholesterol Education Program, Adult Treatment Panel III (NCEP III).11 Physical activity was evaluated by the IPAQ questionnaire, as proposed by the Consensus Group for the Development of an International Physical Activity Questionnaire, formed under the seal of the World Health Organization.12 The local Ethics Committee approved the protocol (Complexo Hospitalar - Hospital Universitário Os-waldo Cruz - Universidade de Pernambuco / UPE / PROCAPE. CAAE- 53816216.0.0000.5192) and all participants signed the consent form.

Sleep Study

All patients underwent a portable overnight sleep recording in the sleep laboratory using a validated device (Embletta, PDS; Medcare, Reykjavik, Iceland), to evaluate oxygen saturation, body position, measurements of nasal flow (pressure cannula), and measurements of respiratory effort using two respiratory inductance plethysmography belts.

Apnea was defined as a total absence (> 90%) and hypopneas as a decrease in nasal flow for ≥ 10 seconds, accompanied by a 4% oxygen desaturation, respectively.13 The AHI was calculated by dividing the total number of apneas and hypopneas by total time in bed. OSA was defined as an AHI ≥ 5 events/h. The oxygen desaturation index was calculated by dividing the total number of desaturations by total time in bed. In addition, subjective daytime sleepiness was evaluated using the Epworth Sleepiness Scale. An Epworth Sleepiness Scale score > 10 was considered excessive somnolence.14 Moreover, subjective sleep quality was measured by the Pittsburgh Sleep Quality Index, which is a self-report questionnaire that assesses sleep quality and disturbances over a 1-month interval.15

Exercise Testing

All participants underwent exercise testing using Bruce or modified Bruce Protocols16 with standard equipment (Inbramed), and software (Ergo PC - MICROMED Biotechnology). Participants were instructed to eat light foods up to 2 hours before the test, to take medications at the usual doses and times, to abstain from alcohol or stimulants (with caffeine) and exercise for 24 hours before exercise. They were instructed to wear comfortable clothing and shoes suitable for treadmill exercise. Before the start of exercise, subjects stood stationary on the treadmill for approximately 2 minutes for evaluation and recording of the 12-lead resting electrocardiogram, and measurement of resting blood pressure through the auscultatory technique. All tests were performed by a trained physician blinded to the presence or absence of OSA. During exercise and the first 6 minutes of recovery, continuous electrocardiogram recordings, blood pressure measurements (every 3 minutes during exercise and at peak exercise, and at each minute of recovery) were performed. Exercise was terminated for patient-reported exhaustion, or because of the presence of predefined clinical criteria.16

An indirect measurement of peak oxygen uptake (VO2peak) was calculated automatically by standard software, using the American College of Sports Medicine recommendations,16 as well as achieved metabolic equivalent. Patients were classified with low exercise capacity when they could not reach 85% of the age-predicted VO2peak. For those on beta-blockers, reduced exercise capacity was considered when they did not reach 62% of the predicted VO2peak.16

Statistical Analyses

Normality distribution was evaluated with the Kolmogorov-Smirnov test and the results were expressed as mean ± standard deviation, median (interquartile range), or percentage. Pearson chi-square test was applied in the comparisons between proportions, and the analysis of variance or Kruskal-Wallis test was used when appropriate, with Bonferroni post hoc test. Multivariate logistic regression was applied using the stepwise method with the introduction of variables with statistical significance in the bivariate analysis below 20% (P < 0.2). Variables with statistical significance below 10% (P < 0.1) and/ or biological plausibility remained in the model. Logistic regression analysis relating exercise capacity with OSA (AHI > 5 events/h) and obesity (BMI > 30 kg/m2), were adjusted for age, hypertension, diabetes, and physical activity. The estimated association measure was the OR, with their respective CIs. We tested the existence of interaction between apnea and obesity, creating an interaction term with the two variables (BMI > 30 kg/m2 and AHI > 5 events/h). Data were analyzed with Stata (version 11.0, StataCorp LLC, College Station, Texas, United States). A two-sided value of P < .05 was considered significant.


A total of 257 women were screened, of whom 24 refused to participate or did not meet the inclusion criteria, whereas one was excluded because of poor sleep study quality, resulting in a final sample of 232 participants.

The population consisted of predominantly middle-aged and overweight women (Table 1). Mild (AHI ≥ 5 events/h) or moderate/severe (AHI ≥ 15 events/h) OSA was diagnosed in 68 (29.3%) and 22 (9.4%) women, respectively. Obesity (BMI > 30 kg/m2) was present in 76 participants (33%). The clinical and anthropometric characteristics of patients without OSA, with mild OSA, and moderate/severe OSA are summarized in Table 1. When compared to those without OSA, those with OSA had higher BMI, an increased frequency of arterial hypertension, used more antihypertensive drugs, and had worse sleep quality, assessed by Pittsburgh Sleep Quality Index. Excessive daytime sleepiness was not different between groups (Table 2).

Clinical and anthropometric characteristics.


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Table 1

Clinical and anthropometric characteristics.

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Clinical and sleep study assessments.


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Table 2

Clinical and sleep study assessments.

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Patients with moderate to severe OSA were more hypertensive at baseline and during exercise than patients with mild or no OSA (Table 3). There were no differences between groups either in relation to the resting heart rate or in heart rate recovery at 1-minute postexercise. However, peak heart rate was lower in the moderate to severe OSA group, compared with the mild or no OSA groups. This difference remained significant when we excluded from the analysis patients on beta blockers (151 ± 22 versus 153 ± 18 versus 161 ± 19 bpm, P = .01), respectively. The group with moderate to severe OSA had lower achieved METs and higher frequency of reduced exercise capacity when compared to patients with either mild or no OSA (Table 3 and Figure 1).

Exercise test outcomes by OSA category.


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Table 3

Exercise test outcomes by OSA category.

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Frequency of reduced exercise capacity according to OSA diagnosis.

EC = exercise capacity, mod = moderate, OSA = obstructive sleep apnea.


Figure 1

Frequency of reduced exercise capacity according to OSA diagnosis.

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OSA was associated with low exercise capacity in univariate logistic regression. However, in multivariate analysis, the presence of obesity without OSA was associated with low exercise capacity (OR 2.88, 95% CI 1.02–8.11, P = .045), whereas the presence of OSA without obesity was not (OR 1.07, 95% CI 0.31–3.69, P = .912). Nevertheless, the coexistence of obesity and OSA increased markedly the odds of reduction in exercise capacity (OR 9.40, CI 3.79–23.3, P < .001) after adjusting for age, hypertension, diabetes, and sedentary lifestyle. (Table 4). Baseline characteristics of the obese subgroup without and with OSA are presented in Table S1 in the supplemental material. Moreover, interaction between OSA and obesity was clearly demonstrated when analyzing the multivariate model with the significant interaction term (obesity plus OSA), as presented in Table S2 in the supplemental material.

Logistic regression analysis relating the age predicted VO2peak below 85% with OSA and obesity.


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Table 4

Logistic regression analysis relating the age predicted VO2peak below 85% with OSA and obesity.

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This study evaluated exercise capacity in middle-aged women, an important predictor of cardiovascular risk, and how it relates to OSA and obesity. OSA was present in 39% and obesity in 33% of the study population. Low exercise capacity was almost three times more frequent in women with moderate to severe OSA than in women with no OSA. However, the effect of OSA on exercise capacity was not independent of other variables such as obesity. Indeed, obesity was independently associated with an almost threefold odds of reduction in exercise capacity and, when obesity and OSA coexisted, the odds increased to 9.4-fold.

The specific characteristics of the study population are worth noting. We evaluated perimenopausal or postmenopausal subjects, which may itself increase the likelihood of OSA.3 The presence of higher BMI and neck and waist circumferences in patients with OSA in our study have been reported by others.3 Obesity may be a direct contributor to OSA because of upper airway narrowing due to fat accumulation, and an indirect contributor because of reduced lung volumes.17,18 Reduction of lung volume may decrease tracheal traction forces, which predisposes to narrowing of the airway.19 In any event, our data suggest that obesity directly contributes to decreased exercise capacity, independent of the presence of OSA. We did not demonstrate increased Epworth Sleepiness Scale scores in the moderate to severe OSA group, but did note poor sleep quality, which may partially explain the low VO2peak achieved. As it was demonstrated an interaction between OSA and obesity, we decided to analyze the four groups based on OSA and obesity status (Table 4).

In one study with 54 lean subjects (27 patients with OSA, 17 women),8 patients with OSA did not differ from volunteers in functional exercise capacity. The study authors argued that obesity but not OSA might be responsible for the low exercise capacity. In our study, lean women with OSA did not manifest reduced exercise capacity. However, obesity increased the likelihood of reduction in exercise capacity, and the association of obesity and OSA appeared to have a synergistic effect on attenuating exercise capacity, suggesting that obesity is the primary mediator of the decrease in cardiopulmonary capacity observed in female patients with OSA. By contrast, another small study with 34 patients (15 patients with moderate to severe OSA and 19 controls; 22 males) found independent relationships between OSA and reduced exercise capacity. The AHI alone explained 16.1% of the variability observed in predicted VO2peak.20

The largest study evaluating exercise capacity in patients with and without OSA was conducted by Mansukhani et al.21 with 1,424 individuals (75% men), performed retrospectively in a tertiary referral center, which showed a negative correlation between AHI and functional exercise capacity. Patients with OSA were older, more obese, and more hypertensive than patients without OSA.

Our study has some limitations. First, due to study design, we could not infer any causality, but only an association between OSA and low functional capacity for exercise. Moreover, our findings relate only to premenopausal and postmenopausal middle-aged women, and we cannot extrapolate our results to the general male or female population. Indeed, prior studies showing OSA as a predictor of reduced exercise tolerance included mostly males, suggesting the possibility of differential effects of sex on the OSA-exercise capacity relationship. Strengths of our work are selection of consecutive patients from gynecological clinics not referred for sleep studies and the use of a standard protocol, which decreases methodological bias.

In conclusion, our study showed an independent effect of obesity on exercise capacity in premenopausal and postmenopausal women, with a potentiating of this effect by the presence of OSA. OSA and obesity may thus together contribute to reduce functional capacity, resulting in heightened cardiovascular risk in middle-aged women.


This work was supported by the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) and CNPq (National Counsel of Technological and Scientific Development). All authors have seen and approved the manuscript. Dr. Somers has served as a consultant for ResMed, Phillips, GlaxoSmithKline, Respicardia, Dane Garvin, Bayer, Itamar, and U-Health. He has spoken at meetings sponsored by Phillips and ResMed. He works with Mayo Health Solutions and their industry partners on intellectual property related to sleep and to obesity. The other authors report no conflicts of interest.



apnea-hypopnea index


body mass index


blood pressure


obstructive sleep apnea


peak oxygen uptake


The authors thank the entire team at PROCAPE Hospital, Recife, Pernambuco, Brazil. Authors' contributions: Martinha Millianny Barros de Carvalho: Data analysis and manuscript draft; Ricardo Quental Coutinho: study design, data analysis; Isly Maria L. Barros: study design, data analysis, and manuscript draft; Laura O. B. F. Costa: study design, data analysis and manuscript draft; Ana Kelley L. Medeiros: data collection; Thais C Lustosa: data collection; Carolina A. Medeiros: data collection; Marcus Vinícius França: data collection; Tarcya L. G. Couto: data collection; Ulisses R. Montarroyos: statistical analysis; Virend K. Somers: manuscript draft; Rodrigo Pinto Pedrosa: study design, data analysis, and manuscript draft.



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Supplemental Material

Supplemental Material

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