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Journal of Clinical Endocrinology & Metabolism, doi:10.1210/jc.2005-1084
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The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 1 36-42
Copyright © 2006 by The Endocrine Society

Relationships between Sleep Disordered Breathing and Glucose Metabolism in Polycystic Ovary Syndrome

Esra Tasali, Eve Van Cauter and David A. Ehrmann

Department of Medicine, University of Chicago, Chicago, Illinois 60637

Address all correspondence and requests for reprints to: David A. Ehrmann, M.D., Department of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 1027, Chicago, Illinois 60637. E-mail: dehrmann{at}uchicago.edu.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Discussion
 References
 
Context: Women with polycystic ovary syndrome (PCOS) are insulin resistant and are at increased risk for sleep apnea, which, in turn, may contribute to insulin resistance.

Objective: The objective of this study was to determine relationships between risk and severity of obstructive sleep apnea (OSA) and glucose metabolism in PCOS.

Design and Setting: This study included two cohorts of women with PCOS in a tertiary care hospital.

Patients and Main Outcome Measures: Cohort 1 included 40 nondiabetics who completed the Epworth Sleepiness Scale, the Pittsburgh Sleep Quality, and the Berlin Questionnaire to assess risk of OSA; 32 of the 40 women had an oral glucose tolerance test. Cohort 2 included eight women who had a sleep study, glycosylated hemoglobin level, and an oral glucose tolerance test.

Results: In cohort 1, 62.5% of the women had poor sleep quality by Pittsburgh Sleep Quality Index, and 18 (45%) had chronic daytime sleepiness by Epworth Sleepiness Scale. Thirty of the 40 women had a high risk of OSA by Berlin Questionnaire. Women with high OSA risk had higher fasting insulin levels and homeostasis model assessment index compared with those with low OSA risk (168.2 ± 17.3 vs. 97.2 ± 6.4 pmol/liter, P = 0.011; 6.3 ± 0.7 vs. 3.6 ± 0.3 mg/dl·µU/ml, P = 0.014, respectively). Among women with normal glucose tolerance, insulin levels were significantly higher in those at high vs. low OSA risk, independently of body mass index. Women in cohort 2 had rapid eye movement (REM)-predominant OSA with lower sleep efficiency, longer sleep latency, and less REM sleep than controls. Glycosylated hemoglobin levels and the area under the glucose curve positively correlated with the apnea-hypopnea index (rP = 0.82, P = 0.013; rP = 0.96, P = 0.0008, respectively) and the number of oxygen desaturations in REM sleep (rP = 0.97, P = 0.0009; rP = 0.97, P = 0.005, respectively).

Conclusion: PCOS is associated with poor sleep quality, daytime sleepiness, and increased risk for OSA. Insulin levels and measures of glucose tolerance in PCOS are strongly correlated with the risk and severity of OSA.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Discussion
 References
 
WOMEN WITH POLYCYSTIC ovary syndrome (PCOS) are more insulin resistant than weight-matched controls and have higher rates of impaired glucose tolerance and type 2 diabetes (1, 2, 3, 4) as well as an increased risk for hypertension (5), dyslipidemia (6), and vascular disorders (7, 8, 9).

Sleep-disordered breathing (SDB) is characterized by repetitive episodes of partial or complete upper airway obstruction during sleep. Obesity and male gender are the most important risk factors (10). The male-to-female ratio in prevalence of SDB may be as high as 10:1 (11). SDB is uncommon in women before menopause (0.6%) (12). Among women with PCOS, however, the prevalence of SDB may be 30–40 times that observed in age- and weight-matched controls (13, 14, 15).

In non-PCOS populations, there is increasing evidence for a link among SDB, insulin resistance, and the risk for the metabolic syndrome independently of the degree of obesity (16, 17, 18, 19, 20, 21, 22, 23, 24, 25).

SDB is invariably associated with sleep fragmentation and sleep loss. There is growing evidence from laboratory and epidemiological studies for an adverse effect of sleep loss or sleep disturbances on carbohydrate metabolism (17, 18, 19, 21, 22, 23, 26, 27, 28, 29, 30). Thus, there could be a link between the high prevalence of SDB in PCOS and the severity of insulin resistance and increased diabetes risk in these women. So far, only one study has explored the relationships between glucose metabolism and the presence of SDB in PCOS (13). PCOS women with SDB had higher fasting insulin levels than those without SDB, independently of body mass index (BMI). Glucose tolerance and the severity of SDB were not assessed. The present study examines the relationships among subjective sleep quality, SDB, and measures of glucose tolerance and insulin resistance in PCOS.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Discussion
 References
 
Patient population

This study was approved by the University of Chicago Institutional Review Board, and all participants gave written informed consent. Patients were recruited from the Endocrinology Clinics at the University of Chicago. A diagnosis of PCOS required (31): 1) the presence of oligo-/amenorrhea; 2) hyperandrogenemia, defined by a supranormal plasma free testosterone (T) level (>10 pg/ml); 3) hyperandrogenism, as evidenced by infertility, hirsutism, acne, or androgenetic alopecia; and 4) exclusion of nonclassic 21-hydroxylase deficiency congenital adrenal hyperplasia, Cushing’s syndrome, hypothyroidism, or elevated serum prolactin.

Data obtained from two cohorts of PCOS women were analyzed. Cohort 1 was comprised of 40 women in whom subjective sleep quality was assessed using three well-validated sleep surveys [Pittsburgh Sleep Quality (PSQ) questionnaire, Berlin Questionnaire (BQ), and Epworth Sleepiness Scale (ESS)]. The surveys were mailed to 200 PCOS patients who had had at least one outpatient visit in the preceding 24 months. Delivery to the current mailing address was verified in 163 patients. The 40 respondents were similar with respect to ethnic background, age, and BMI to the patient population of our clinics, based on our database of 629 PCOS women. The responders were on average 3 yr older and heavier than the nonresponders. An oral glucose tolerance test (OGTT) had been obtained in 32 of these 40 patients at the time of initial PCOS diagnosis, before the initiation of treatment. BMI was calculated from the height and weight measured at the time of OGTT.

Cohort 2 included eight PCOS women in whom sleep quality was assessed by overnight polysomnography (PSG). These patients were referred to the University of Chicago Clinical Sleep Laboratory due to presence of symptoms (e.g. snoring, daytime sleepiness) suggestive of obstructive sleep apnea. An OGTT had been obtained in each patient at the time of initial PCOS diagnosis, before the initiation of treatment. Levels of glycosylated hemoglobin (HbA1c) and of total and free T were measured on a baseline blood sample.

Normative values for polysomnographic parameters were derived from studies conducted in nonobese women who volunteered as healthy controls. For the present analyses, PSG data obtained in eight healthy women who were age-matched (31.0 ± 2.1 vs. 30.5 ± 1.9 yr, P = 0.863) to the PCOS women in cohort 2 were used.

Survey assessment of sleep quality, risk of sleep apnea, and daytime sleepiness

Sleep quality, risk of sleep apnea, and daytime sleepiness were assessed using three self-administered questionnaires. The PSQ includes 21 items that assess sleep quality and disturbances over the previous month (32). Seven component scores ranging each from 0–3 are generated. The global index score, the PSQ Index (PSQI), is the sum of these seven scores. The components include: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. A global PSQI greater than 5 has a diagnostic sensitivity of 89.6 and specificity of 86.5 in distinguishing good from poor sleepers (32).

The BQ is a validated survey assessing the risk for sleep apnea that includes questions about snoring behavior (category 1), chronic daytime sleepiness (category 2), and the presence of hypertension and/or BMI exceeding 30 kg/m2 (category 3). This instrument predicts high risk for sleep apnea with a sensitivity of 0.86, specificity of 0.77, a positive predictive value of 0.89, and a likelihood ratio of 3.2 (33). Because obesity and hypertension are present in the majority of PCOS women, we used a modified scoring that excluded responses in category 3. Each of the other categories was assigned a score of either 0 for no symptoms, 1 for frequent symptoms (<3–4 times a week), or 2 for persistent symptoms (≥3–4 times a week). To be considered as high risk for sleep apnea, a patient had to have a cumulative score of 2 or higher.

The ESS is a validated survey assessing daytime sleepiness (34) that asks to rate (on a scale of 0–3) the likelihood of falling asleep in eight different circumstances. A global score of 10 or greater is indicative of a significant degree of daytime sleepiness.

PSG

PSG (Neurofax EEG 1100, Nihon Kohden, Foothill Ranch, CA) involved the recording of two central and two occipital electroencephalograms, bilateral electroculograms, submental electromyogram, leg movements, electrocardiogram, oronasal airflow, respiratory effort, and arterial oxygen saturation by pulse oximetry.

Recordings were visually scored in 30-sec epochs according to standard criteria (35). Obstructive events (i.e. apneas and hypopneas) and microarousals were scored according to established criteria (36, 37). The total apnea-hypopnea index (AHItotal) is the total number of apneas and hypopneas during sleep divided by the total sleep time in hours. AHIREM and AHINREM are the total number of apneas and hypopneas during rapid eye movement (REM) and non-REM (NREM) sleep per hour of REM and NREM sleep, respectively. The microarousal index (MAI) is derived for both REM (MAIREM) and NREM (MAINREM) sleep by dividing the total number of microarousals during REM and NREM sleep per hour of REM and NREM sleep, respectively. Total oxygen desaturation index (ODItotal) is the total number of oxygen saturation drops from baseline of at least 3% during sleep per total sleep time in hours. The oxygen desaturation index during REM sleep (ODIREM) and NREM sleep (ODINREM) is the total number of oxygen saturation drops from baseline of at least 3% during REM sleep and NREM sleep per REM and NREM sleep time in hours. Two patients had missing values for oxygen saturation indices due to a failure of oximetry, and one subject had missing values for arousal indices due to a software error.

OGTT

After an overnight 12-h fast, baseline samples were obtained at –15 and 0 min for measurement of glucose and insulin concentrations. At time 0 min, 75 g glucose was administered orally, and blood samples were collected at 30, 60, 90, 120, 150, and 180 min. A diagnosis of normal glucose tolerance, impaired glucose tolerance, or diabetes was assigned if the glucose level at 2 h was less than 140 mg/dl, between 140 and 200 mg/dl, or 200 mg/dl or more, respectively (38). Areas under the glucose (AUCglu-3 h) and insulin (AUCins-3 h) responses were calculated for the 3-h interval postglucose ingestion using the trapezoidal rule. Insulin resistance was estimated by homeostasis model assessment (HOMA) index as fasting serum insulin x fasting plasma glucose)/22.5 (39).

The mean time interval between the OGTT and questionnaire completion was three years (range, 0.1–11.9 yr).

Assays

Plasma glucose was assayed by the glucose oxidase method, and serum insulin was assayed by a double-antibody RIA. A plasma sample was obtained at 0800 h at baseline for measurement of HbA1c, total T, and free T. Total T was measured using a kit from Diagnostic Products (Los Angeles, CA). The free fraction of plasma T was measured by a competitive protein-binding assay (40). The intra- and interassay coefficients of variation (CV) are 3.8 and 8.7%, respectively. HbA1c was measured by Bio-Rad Variant Classic boronate affinity-automated HPLC (Bio-Rad, Hercules, CA). The intraassay CV was 0.5–1.0%, and the interassay CV ranges from 2.2–2.4%.

Statistical analysis

Group values are expressed as mean ± SEM. The Student’s t test was used for between-group comparisons. Correlations between sleep and metabolic variables were estimated using the Pearson coefficient (rP). ANOVA (type III sum of squares) was used to analyze relationships among metabolic parameters, sleep variables, and BMI. All calculations were performed using StatviewSE+ and SuperANOVA software (version I.II; SAS Institute, Inc., Cary, NC).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Discussion
 References
 
Cohort 1

Subjects (n = 40). Mean age and BMI were 31.4 ± 1.2 yr and 39.8 ± 1.3 kg/m2, respectively. Patients’ records at the time of survey revealed that seven (18%) of the 40 women had a prior diagnosis of sleep apnea. No other patient had a prior diagnosis of any sleep disorder. Seven (18%) of 40 patients had depression/anxiety, five (13%) had hypertension, four (10%) had hypothyroidism, and two (5%) had asthma. All patients with comorbid conditions were receiving treatment and were in stable condition. Twenty-six patients were treated with metformin and/or sex steroids (oral contraception) for PCOS.

Subjective sleep quality. Twenty-five (63%) of the 40 respondents had a global PSQI greater than 5, the cutoff point for poor sleep quality. The mean PSQI was 7.8 ± 0.7. Eighteen of 40 (45%) had an Epworth score of 10 or greater, indicative of chronic daytime sleepiness (mean Epworth Score, 8.8 ± 0.6). Responses to the BQ revealed that 30 (75%) of 40 women were at high risk for sleep apnea.

Figure 1Go illustrates the prevalence of apnea risk and sleep complaints in this cohort of 40 PCOS women. Thirty-seven of the women (> 92%) had either a high risk of sleep apnea or an abnormal score on the PSQI or the ESS.



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  		FIG. 1. The frequency distribution of the risk for sleep apnea (derived from responses to the BQ) and sleep complaints derived from responses to the PSQ questionnaire and ESS in women with PCOS. A single sleep complaint was defined as a PSQI greater than 5 or an ESS score equal to or greater than 10.

 
Glucose metabolism and risk of sleep apnea. Glucose tolerance was determined by OGTT in 32 of the 40 patients (age, 30.5 ± 1.0 yr; BMI, 39.1 ± 1.5 kg/m2) and was normal in 19 (59%) and impaired in 13 (41%) subjects.

Twenty-two of the 32 women (69%) were at high apnea risk. Those at high risk had higher fasting insulin levels than those at low risk (168.2 ± 17.3 vs. 97.2 ± 6.4 pmol/liter, P = 0.011). Fasting insulin levels were positively correlated (rP = 0.74) to both apnea risk (P = 0.003) and BMI (P = 0.004). The interaction term (apnea risk x BMI) was highly significant (P = 0.0005), reflecting the fact that the association between BMI and insulin levels was significant only in the high-risk group (rP = 0.75, P = 0.0001 vs. rP = –0.42, P = 0.23) (Fig. 2AGo). Similarly, the HOMA index was higher in patients at high vs. low risk (6.3 ± 0.7 vs. 3.6 ± 0.3 mg/dl·µU/ml, P = 0.014). ANOVA revealed significant associations between the HOMA index, risk for sleep apnea (P = 0.002), and BMI (P = 0.008). The interaction term was also significant (P = 0.0004), indicating that the relationship between BMI and this measure of insulin resistance was significant only in patients at high risk (rP = 0.74, P = 0.0001 vs. rP = –0.54, P = 0.107) (Fig. 2BGo).



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  		FIG. 2. A, Relationships between BMI and fasting insulin levels in women with PCOS at high (left panel) and low (right panel) risk for sleep apnea. B, Relationships between BMI and fasting HOMA index in women with PCOS at high (left panel) and low (right panel) risk for sleep apnea.

 
In subjects with normal oral glucose tolerance (n = 19), glucose levels during the OGTT were similar for those at high vs. low apnea risk, whereas insulin levels were consistently higher in patients at high risk (Fig. 3AGo). Importantly, the AUCins-3 h was not predicted by BMI (P = 0.519) but appeared related to the apnea risk (P = 0.059). The interaction between BMI and apnea risk was not significant. In contrast, in subjects with impaired glucose tolerance (n = 13), glucose and insulin profiles during OGTT were similar in women with high and low apnea risk (Fig. 3BGo).



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  		FIG. 3. A, Mean (±SEM) profiles of glucose (upper panel) and insulin (lower panel) concentrations during the OGTT in women with PCOS with normal glucose tolerance. B, Mean (±SEM) profiles of glucose (upper panel) and insulin (lower panel) concentrations during the OGTT in women with PCOS with impaired glucose tolerance. Wide lines, PCOS women at high risk for sleep apnea; narrow lines, PCOS women at low risk for sleep apnea.

 
We also explored the relationships between apnea risk and fasting insulin levels and HOMA index using risk of sleep apnea as a continuous rather than dichotomous variable. There was a strong and significant relationship between the score of risk of sleep apnea and both the insulin concentration (r = 0.655, P = 0.0001) and HOMA index (r = 0.653, P = 0.0001).

Cohort 2

Subjects (n = 8). Demographic, metabolic, and hormonal characteristics are shown in Table 1Go. Glucose tolerance was impaired in six patients, and one patient was diabetic. Six of the eight patients were treated for comorbidities (diabetes, hypertension, or depression), and all patients were in stable condition when PSG was performed.


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  		TABLE 1. Demographic, metabolic, and hormonal characteristics of women with PCOS (cohort 2, n = 8)

 
Sleep variables. Sleep architecture variables from PCOS women in cohort 2 and age-matched healthy nonobese women are reported in Table 2Go. Compared with reference values, women with PCOS had decreased total sleep time, lower sleep efficiency, longer sleep latency, increased wake time, and lower amounts of REM and NREM sleep.


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  		TABLE 2. Sleep architecture of women with PCOS (cohort 2) and age-matched healthy nonobese women

 
The measures of severity of SDB in PCOS women are reported in Table 3Go. SDB occurred predominantly during REM sleep. This pattern of SDB is reflected by the significant (P = 0.0003) difference between the AHI derived during REM (AHIREM) and NREM (AHINREM) sleep phases. An AHItotal greater than 5 per hour of sleep is considered as abnormal (10). Sleep was fragmented by microarousals that were also more frequent during REM than during NREM sleep (MAIREM vs. MAINREM, P = 0.033). Normative arousal indices of less than 15 per hour during both NREM and REM have been reported previously for adults aged 20–39 yr (41). Oxygen desaturations occurred mostly during REM sleep and were associated with obstructive respiratory events (ODIREM vs. ODINREM, P = 0.001). These differences in sleep variables between REM and NREM sleep were not due to medications used to treat PCOS or its comorbidities.


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  		TABLE 3. Severity of sleep-disordered breathing in women with PCOS (cohort 2, n = 8)

 
Glucose metabolism and severity of sleep apnea. In this group of PCOS women, measures of glucose tolerance were strongly associated with the severity of SDB, as quantified by the AHItotal. The upper left panel of Fig. 4Go illustrates the positive correlation between HbA1c and AHItotal in all eight women (rP = 0.82, P = 0.013). Because the value for the AUCglu-3 h in the one subject with diabetes was a statistical outlier (42), it was excluded from this analysis. The lower left panel of Fig. 4Go shows that, in nondiabetic PCOS women, more than 90% of the variability in the glucose response to OGTT is accounted for by the degree of severity of SDB (rP = 0.96, P = 0.0008). Similar highly significant correlations were obtained when data from the single diabetic subject were included.



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  		FIG. 4. Relationships between the markers of severity of SDB and measures of glucose tolerance in women with PCOS. Left upper panel, Relationship between AHItotal and HbA1c. Left lower panel, Relationship between AHItotal and the AUCglu-3 h. Right upper panel, Relationship between total number of desaturations in REM sleep and HbA1c. Right lower panel, Relationship between total number of desaturations in REM sleep and AUCglu-3 h.

 
Because SDB occurred mostly during REM sleep, we further explored the relationships between sleep fragmentation and hypoxia in REM sleep and measures of glucose tolerance. No significant associations were found with AHIREM and MAIREM. However, when the total number of desaturations during REM sleep was considered, strong associations with both HbA1c (rP = 0.97, P = 0.0009; upper right panel of Fig. 4Go) and AUCglu-3 h (rP = 0.97, P = 0.005; lower right panel of Fig. 4Go) emerged. Similar robust correlations between HbA1c and AUCglu-3 h, on the one hand, and the total number of microarousals during REM were also present (rP = 0.99, P = 0.0001, rP = 0.78, P = 0.065; respectively). All associations illustrated in Fig. 4Go remained highly significant when BMI was controlled for by ANOVA.

In this group of PCOS women with variable degrees of glucose tolerance, we did not detect consistent correlations between insulin levels and measures of severity of SDB. We also failed to detect significant correlations between androgen levels and severity of SDB.


   Discussion
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
References
 
Insulin resistance is a key component of the pathogenesis of PCOS (43). Recently, SDB has been identified as an important risk factor for insulin resistance, independent of BMI (16, 17, 18, 19, 20, 21, 22, 23). Although SDB is typically a disorder of older overweight and obese men, three separate reports involving a total of nearly 100 patients, have indicated that SDB is highly prevalent in premenopausal women with PCOS (13, 14, 15).

Based on both indirect (survey-derived) and direct (PSG) measures of sleep quality, the present study provides novel evidence for a relationship between insulin levels and risk of sleep apnea and demonstrates that in PCOS women, glucose tolerance is directly related to the severity of SDB.

Forty of 163 PCOS patients responded to our surveys. Androgen levels, and measures of glucose metabolism were similar between responders and nonresponders. Our survey revealed that three of four women with PCOS were at high risk for sleep apnea and that fewer than 8% were free of sleep problems. These striking proportions suggest that a sleep disorder may be an intrinsic component of PCOS. Fasting insulin levels and HOMA index were markedly increased in women at high risk for sleep apnea. Furthermore, the well-documented relationship between insulin resistance and BMI was present only in the women at high apnea risk.

So far, only one previous study (13) has explored the relationships between fasting insulin levels and the presence of SDB in PCOS. Consistent with our findings, PCOS women with SDB had higher insulin levels than those without SDB, independently of BMI (13). Insulin resistance appeared to be the strongest predictor of SDB in this patient population, even after controlling for age, BMI, and free and total T levels.

One unique aspect of our study is the analysis of glucose and insulin responses during an OGTT in PCOS women at high and low risk for sleep apnea. In women with normal glucose tolerance, the insulin response, quantified by the AUC, was more than 2-fold greater in women at high risk vs. those at low risk. One possible implication is that SDB may exacerbate the adverse metabolic consequences of insulin resistance and accelerate the conversion from normal to impaired glucose tolerance. Previous reports have indicated women with PCOS develop impaired glucose tolerance or diabetes at an earlier age than similarly obese women without PCOS (4, 44, 45, 46).

The most intriguing finding of our study is the strong association between measures of glucose tolerance and severity of SDB. Indeed, among nondiabetic PCOS women, more than 90% of the variability in the glucose response to OGTT was accounted for by the severity of SDB as assessed by the AHItotal. Similarly, more than 90% of the variability in HbA1c was predicted by the number of oxygen desaturations in REM sleep. These findings could indicate that glucose tolerance and SDB are strongly influenced by a common mechanism in PCOS. They are consistent with the rapidly accumulated evidence in non-PCOS populations showing that SDB is independently associated with adverse metabolic abnormalities including glucose intolerance, insulin resistance, diabetes, hypertension, and dyslipidemia (18, 19, 25, 30).

Similar to Vgontzas et al. (13), we found no significant relationships between androgen levels in PCOS and the severity of SDB. In contrast, Fogel et al. (14) reported that AHI was positively correlated with both serum androgen levels and the waist to hip ratio in PCOS women and suggested that central obesity, rather than androgen excess, could be the predictor of SDB in PCOS.

Our finding that SDB occurred predominantly during REM sleep was consistent with the report of Fogel et al. (14). Sympathetic activity is typically higher in REM sleep than NREM sleep and is further exacerbated with recurrent upper airway obstruction and hypoxemia. This increased sympathetic output in REM sleep may underlie the relationship between SDB, insulin resistance, and glucose intolerance in PCOS (47).

We found that PCOS women had lower sleep efficiency, longer sleep latency, and increased time awake than healthy age-matched controls, suggesting that they may chronically suffer from insufficient sleep. Moreover, frequent arousals that occur in SDB are also likely to produce a cumulative sleep debt. Laboratory studies of healthy adults (26) and epidemiological studies (27, 28) have demonstrated a link between insufficient amounts of sleep and decreased glucose tolerance. Thus, a state of sleep debt may also contribute to the adverse metabolic consequences in PCOS.

Our study has several limitations. First, the sample size was relatively small, and the study involved a time lapse between measures of glucose metabolism and sleep variables. We verified that the relationship among fasting insulin levels, HOMA index, and sleep apnea risk remained essentially unchanged when the time between the OGTT and the return of the surveys was accounted for by ANOVA. Thus, despite the relatively small sample size and the variation in time between the collection of metabolic and sleep data, robust associations between measures of glucose metabolism and sleep variables were detected. Second, as in most PCOS populations, obesity was a confounding factor in our two cohorts of women. Therefore, we controlled for BMI in the statistical analysis and used a modification of the BQ that excluded BMI in the assessment of apnea risk. Finally, although medication use could presumably influence our findings, we could not detect any significant association between sleep variables and medications used to treat PCOS and/or its comorbidities.

In summary, the majority of PCOS women appear to have poor sleep quality, excessive daytime sleepiness, and a high risk for sleep apnea. In PCOS women, there are remarkably robust associations between the degree of impairment of glucose tolerance and the severity of SDB. Causal mechanisms remain to be elucidated. Sleep disturbances thus appear to be an important feature in the pathogenesis of metabolic sequelae in PCOS. Given that treatment of SDB with continuous positive airway pressure can improve glucose metabolism in non-PCOS populations (21, 22), we suggest that women with PCOS should be systematically evaluated for the risk of sleep apnea.


   Footnotes
 
This work was supported by the National Institutes of Health (Grants M01-RR-00055, DK-41814, AG-11412, HL-075079, and P60-DK20595), a Clinical Research Award (to D.A.E.) from the American Diabetes Association, and a gift from the Blum-Kovler Foundation.

First Published Online October 11, 2005

Abbreviations: AHINREM, Total number of apneas and hypopneas during NREM sleep per hour of NREM sleep; AHIREM, total number of apneas and hypopneas during REM sleep per hour of REM sleep; AHItotal, apnea-hypopnea index; AUCglu-3 h, area under the glucose curve; AUCins-3 h, area under the insulin curve; BMI, body mass index; BQ, Berlin Questionnaire; CV, coefficient(s) of variation; ESS, Epworth Sleepiness Scale; HbA1c, glycosylated hemoglobin; HOMA, homeostasis model assessment; MAI, microarousal index; NREM, non-REM; ODI, oxygen desaturation index; OGTT, oral glucose tolerance test; PCOS, polycystic ovary syndrome; PSG, polysomnography; PSQ, Pittsburgh Sleep Quality; PSQI, PSQ Index; REM, rapid eye movement; SDB, sleep-disordered breathing; T, testosterone.

Received May 16, 2005.

Accepted October 4, 2005.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 

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A. N VGONTZAS, E. O BIXLER, and G. P CHROUSOS
Obesity-Related Sleepiness and Fatigue: The Role of the Stress System and Cytokines
Ann. N.Y. Acad. Sci., November 1, 2006; 1083(1): 329 - 344.
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