Objective: The aim of the study was to compare the comorbidities and sleep patterns most commonly associated with each gender in obstructive sleep apnea (OSA). Methods: This was a cross-sectional study of obese individuals with OSA. The polysomnographies were carried out in a sleep laboratory environment, using a 15-channel polysomnography setup. Airflow was measured using a nasal pressure cannula/thermistor combination. A standard handbook was used for interpretation of PSG findings. Results: A total of 284 subjects were included in the study, (147 females). The mean age, body mass index and neck circumference were similar between females and males ( p = 0.9579, p < 0.0001, and p < 0.0001, respectively). On polysomnography, females exhibited longer latency to REM sleep (146.50 ± 85.93 vs. 122.3 ± 68.28, p = 0.0210) and a higher percentage of delta sleep (10.09 ± 7.48 vs. 7.55 ± 6.57, p = 0.0037); males had more frequent microarousals (38.37 ± 27.44 vs. 28.07 ± 21.23, p = 0.0017) and a higher AHI score (30.56 ± 27.52 vs. 17.31 ± 21.23, p < 0.0001). The comorbidities most commonly associated with female gender were diabetes (29% vs. 9.49%, p = 0.0132), hypothyroidism (20% vs. 2.19%, p < 0.0001), and depression (81.63% vs. 51.22%, p < 0.0001). Male gender was associated with myocardial infarction (6.57% vs. 1.38%, p = 0.0245) and alcohol intake (33.88% vs. 11.34%, p < 0.0001). Obese males with OSA have a larger neck circumference and higher AHI and arousal indices than females. Conclusions: There are genderdifferences both in the sleep patterns and in the comorbidities of patients with OSA. Men had a larger neck circumference, higher apnea and sleep fragmentation scores, were more likely to consume alcohol, and were more likely to have a history of myocardial infarction than women.
OSA (obstructive sleep apnea), the most common respiratory disorder of sleep, is caused by the loss of upper airway dilating muscle activity during sleep superimposed on a narrow upper airway. This results in recurrent nocturnal asphyxia. Termination of these events usually requires arousal from sleep and results in sleep fragmentation and hypoxemia, which leads to poor quality sleep, excessive daytime sleepiness, reduced quality of life and numerous other serious health consequences [
OSA and obesity are two major public health issues, both of which have become increasingly common over the past few decades [
Changes in sleep architecture are also quite common in these patients. Studies published in 2009 by Rao et al. [
The present study sought to determine the comorbidities most commonly associated with OSA in each gender and conduct a polysomnography (PSG)-based comparison of sleep architecture in a sample of 284 obese men and women.
This was a cross-sectional chart review study of obese individuals who underwent overnight PSG at the sleep disorders laboratory of Brasília University Hospital between 2007 and 2010.
The study was analyzed and approved by the local research ethics committee (institutional review board- equivalent).
All patients provided written informed consent for participation. The study sample comprised 284 participants, 137 males (48.2%) and 147 females (51.8%), with a body mass index (BMI) ≥ 30 kg/m2. Patients with narcolepsy or idiopathic hypersomnia, neuromuscular disorders, psychiatric disorders, or severe heart or lung disease were excluded from the sample, as were pregnant women and participants whose PSGs were affected by technical difficulties.
The following comorbidities were selected for assessment and comparison: arterial hypertension, cardiac arrhythmias, acute myocardial infarction, congestive heart failure, angina diabetes mellitus, hypothyroidism, depression, asthma, and chronic obstructive pulmonary disease (COPD). The prevalence of two relevant social habits―alcohol intake, defined as regular intake of alcoholic beverages at least once weekly, and smoking, defined as self-reported daily use of tobacco―was also assessed, and Epworth Sleepiness Scale (ESS) scores were calculated for all participants.
All studies were carried out under the supervision of trained polysomnography technologists, in a sleep laboratory environment, using a 15-channel polysomnography setup. Airflow was measured using a nasal pressure cannula/thermistor combination. Hypopnea was defined as a 30% reduction in respiratory flow with a concomitant > 4% reduction in oxygen saturation (SaO2). A standard handbook was used for analysis and interpretation of PSG findings.
The following polysomnography parameters were selected for analysis and comparison: sleep latency (LAT), latency to REM sleep (LATREM), lowest recorded SaO2 (SATMIN), percentage of total sleep time (TST) spent below SaO2 < 90% (T90), percentage of TST spent in delta sleep (%Delta), percentage of TST spent in REM sleep (%REM), apnea-hypopnea index (AHI), and number of microarousals per hour of sleep (MICRO).
Continuous variables were expressed as means and standard deviations, and categorical variables, as relative frequencies (percentages). The Student t-test was used for between-gender comparison of normally distributed continuous variables (as determined by the Kolmogorov-Smirnov test). The Mann-Whitney U was used in case of non-normal distribution. The chi-square or Fisher’s exact tests were used as appropriate for between-gender comparison of categorical variables. The significance level was set at p < 0.05. All statistical analyses were performed in the SAS 9.2 for Windows software package.
The variables of interest were classified as anthropometric and social (
Variable | Overall | Males | Females | p |
---|---|---|---|---|
Age (years) | 45.65 ± 13.98 | 45.69 ± 14.48 | 45.60 ± 13.56 | 0.9579 |
BMI (kg/m2) | 37.80 ± 7.22 | 35.56 ± 5.37 | 39.89 ± 8.07 | <0.0001 |
NC (cm) | 41.81 ± 4.22 | 44.51 ± 3.10 | 39.26 ± 3.50 | <0.0001 |
ESS score (points) | 10.44 ± 4.90 | 10.13 ± 4.64 | 10.74 ± 5.15 | 0.3799 |
Alcohol intake (%) | 23.85 | 33.88 | 11.34 | 0.0001 |
Smoking (%) | 9.13 | 9.84 | 8.25 | 0.6852 |
Variables expressed as mean ± standard deviation unless otherwise noted. BMI, body mass index; ESS, Epworth Sleepiness Scale; NC, neck circumference.
Variable | Overall | Males | Females | p |
---|---|---|---|---|
Lat (min) | 18.68 ± 26.00 | 18.82 ± 29.69 | 18.55 ± 22.14 | 0.5934 |
LatREM (min) | 134.58 ± 78.53 | 122.30 ± 68.28 | 146.50 ± 85.93 | 0.0210 |
%Delta (%) | 8.87 ± 7.15 | 7.55 ± 6.57 | 10.09 ± 7.48 | 0.0037 |
%REM (%) | 17.54 ± 8.12 | 18.27 ± 7.93 | 16.86 ± 8.28 | 0.1447 |
SatMIN (%) | 76.42 ± 12.36 | 76.35 ± 11.60 | 76.48 ± 13.09 | 0.5030 |
T90 (%) | 23.08 ± 31.72 | 18.81 ± 26.67 | 27.12 ± 35.47 | 0.8711 |
AHI (n/h) | 23.70 ± 24.19 | 30.56 ± 27.52 | 17.31 ± 18.54 | <0.0001 |
MICRO (n/h) | 33.04 ± 24.91 | 38.37 ± 27.44 | 28.07 ± 21.23 | 0.0017 |
Lat, sleep latency; LatREM, latency to REM sleep; %Delta, percentage of sleep time spent in delta sleep; %REM, percentage of sleep time spent in REM sleep; SatMIN, lowest recorded oxygen saturation; T90, percentage of recorded sleep time spent with oxygen saturation < 90%; IAH, apnea- hypopnea index; MICRO, number of microarousals per hour of sleep.
Variable | Overall | Males | Females | p |
---|---|---|---|---|
Hypertension (%) | 58.30 | 57.66 | 58.90 | 0.8326 |
Acute myocardial infarction (%) | 3.90 | 6.57 | 1.38 | 0.0245 |
Cardiac arrhythmia (%) | 2.50 | 1.46 | 3.45 | 0.4486 |
Congestive heart failure (%) | 0.70 | 0.00 | 1.38 | 0.4986 |
Angina (%) | 0.00 | 0.00 | 0.00 | - |
Stroke (%) | 0.00 | 0.00 | 0.00 | - |
Diabetes mellitus (%) | 14.80 | 9.49 | 29.00 | 0.0132 |
Hypothyroidism (%) | 11.30 | 2.19 | 20.00 | <0.0001 |
Depression (%) | 50.40 | 51.22 | 81.63 | <0.0001 |
Asthma (%) | 20.10 | 21.90 | 18.62 | 0.4934 |
Chronic obstructive pulmonary disease (%) | 1.10 | 0.73 | 1.38 | 1.0000 |
Mean BMI in the overall sample was 37.80 ± 7.22 kg/m2 (35.56 ± 5.37 kg/m2 in men vs. 39.89 ± 8.07 kg/m2 in women, p < 0.0001). There was a significant between-gender difference in neck circumference. Mean circumference was 41.81 ± 4.22 cm overall and 44.51 ± 3.10 cm in men versus 39.26 ± 3.50 cm in women (p < 0.0001). Alcoholism was also significantly more prevalent among male participants (33.88%) versus female subjects (11.34%) (p = 0.0001). The overall prevalence was 23.85%.
There were no significant between-gender differences in age, smoking prevalence, or ESS scores. A detailed list of these variables, including means, standard deviations, and p-values of between-gender comparison, is shown in
Overall, the mean LATREM was 134.58 ± 78.53 minutes (122.30 ± 68.28 minutes in men vs. 146.50 ± 85.93 minutes in women, p = 0.0210). Mean %Delta was 8.87% ± 7.15% (7.55% ± 6.57% in men vs. 10.09% ± 7.48% in women, p = 0.0037). The mean AHI score was 23.70 ± 24.19 events per hour (30.56 ± 27.52 in men vs. 17.31 ± 18.54 in women, p = 0.0001). Finally, the mean number of microarousals was 33.04 ± 24.91 per hour in the sample as a whole (38.37 ± 27.44 in men vs. 28.07 ± 21.23 in women, p = 0.0017).
No other polysomnography parameters were found to have significant gender differences. Means, standard deviations, and p-values of between-gender comparisons are shown in
Overall, 3.9% of participants had a history of myocardial infarction (6.57% of males and 1.38% of females; significant association with male gender, p = 0.0245). Diabetes mellitus was reported by 14.8% of participants― 9.49% of men and 29% of women―and was significantly associated with female gender (p = 0.0132). Hypothyroidism was reported by 11.3% of participants (2.19% of men and 20.00% of women; p < 0.0001). Depression was reported by 50.40% of participants (51.22% of men and 81.63% of women) and was also significantly associated with female gender (p < 0.0001).
No other comorbidities were significantly associated with gender. Means and p-values of between-gender comparison of these conditions are shown in
This study assessed a clinical population of obese adults with a similar number of participants and age distribution across both genders.
The association between OSA and obesity has been conclusively established for decades [
In men, adipose tissue tends to build up predominantly in the abdomen and neck area, versus the hips, thighs, and gluteal area in women [
Gender differences in the daytime sleepiness reported by OSA patients are also a point of contention. In 1993, Young et al. found that sleepiness was more common among women with OSA than in men with the condition [
In our sample, there were no significant between-gender differences in ESS score (10.13 ± 4.64 vs. 10.74 ± 5.15 points, p = 0.379). It bears noting that obesity per se causes sleepiness, regardless of the presence or absence of OSA [
In terms of social habits, alcohol intake was significantly associated with male gender (33.88% vs. 11.34%, p < 0.0001), whereas smoking was equally prevalent in both genders (9.84% vs. 8.25%, p = 0.6852).
Despite clear evidence of the association between hypertension and obesity and OSA alike [
In our sample, hypertension was the most common comorbidity in both genders, with no statistically significant differences. These findings are consistent with those reported by Peppard et al. [
Experimental laboratory studies have demonstrated that decreasing either the amount or quality of sleep decreases insulin sensitivity and decreases glucose tolerance [
Hypothyroidism is quite common in the population as a whole, and the prevalence of OSA is generally higher in patients living with this condition (25% to 35%). Glycosaminoglycan and protein infiltration of the tongue and pharynx, as well as neuropathy-induced changes in the pharyngeal dilator muscles, are considered plausible causes of the increased prevalence of OSA in hypothyroid patients. There have been scattered reports of regression of OSA after thyroxine replacement therapy [
Depression was also significantly associated with female gender in our sample, although its prevalence was exceptionally high in males as well (80% vs. 63%, p < 0.0001). The population-wide prevalence of depression ranges from 5.8% to 8.4% (higher in those living with chronic illness―9.4% to 12.9%). Rezaeitalab et al. [
COPD affects 5 to 15% of adults (particularly those above the age of 50) in industrialized countries. It tends to predominate among men and smokers, with 65% of the latter developing COPD after the age of 65. Studies suggest that the prevalence of OSA in people with COPD is in the region of 14%, and that the prevalence of COPD in those with OSAHS is approximately 11% [
Obstructive sleep apnea (OSA) occurs more commonly in asthma patients than in the general population and can complicate asthma management [
In our PSG-based assessment of sleep architecture, one surprising finding was a higher LATREM in women (146.5 ± 85.93 min vs. 122.30 ± 68.28 min, p = 0.0210), despite their higher prevalence of depression. Shortened latency to REM sleep, a textbook marker of depression, would have been the expected finding. Nevertheless, Armitage et al. [
Unlike Resta et al. [
Regarding the differences of sleep stages between genders, researches showed that REM sleep seems to have the most adverse influence especially in women. There are few data defining the influence of slow-wave sleep in OSAS, but there are studies that describe the OSA severity is lower during the slow-waves [
In our data, the %Delta was low in both groups, it was significantly higher among women than in men (10.09 ± 7.48 vs. 7.55 ± 6.57, p = 0.0037), as reported by Valencia-Flores et al. [
Finally, as reported by other authors [
There were no significant gender differences in any of the other sleep pattern variables assessed (LAT, %REM, SATMIN, T90).
The present study detected gender differences both in the sleep patterns and in the comorbidities of patients with OSA. Men had a larger neck circumference, higher apnea and sleep fragmentation scores, were more likely to consume alcohol, and were more likely to have a history of myocardial infarction than women. Conversely, women tended to be more obese and were more likely to have comorbid diabetes mellitus, hypothyroidism, and depression.
Knowledge of the distinct gender differences in OSA, such as obesity and its associated comorbidities contribute to a greater awareness of the disease, early diagnosis and its therapeutic management. However, further research should be conducted to verify the correlation of the most significant clinical findings and draw a profile of predictive factors, scaling the age and gender.
A limitation of this study is the sample size investigated. A greater number of samples can ensure a representative distribution of the population and thus be considered representative.
The authors declare that they have no conflict of interests.