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Contents: Original Research

Gestational Diabetes Mellitus and Sleep-Disordered Breathing

Bisson, Michèle Bsc; Sériès, Frédéric MD; Giguère, Yves MD; Pamidi, Sushmita MD; Kimoff, John MD; Weisnagel, S. John MD; Marc, Isabelle MD, PhD

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doi: 10.1097/AOG.0000000000000143
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Gestational diabetes mellitus (GDM) occurs in 2–9% of pregnancies.1 Sleep-disordered breathing ranges from discrete symptoms such as nasal congestion and snoring to severe disturbances, including obstructive sleep apnea–hypopnea.2 Sleep-disordered breathing prevalence and severity increase during pregnancy as a consequence of hormonal and physiologic changes.3 This higher prevalence with up to 25% of pregnant patients reporting pregnancy-onset snoring in the third trimester indicates that pregnancy may be associated with obstructive sleep apnea–hypopnea and hypoxic episodes and might potentially be responsible for adverse perinatal outcomes such as GDM and gestational hypertension or preeclampsia.4,5 Indeed, the combination of altered sleep architecture, sleep fragmentation, and intermittent hypoxia along with sleep-disordered breathing or other factors such as heightened risk of restless legs syndrome6 may increase insulin resistance and weight gain and consequently increase the risk of GDM.2,7,8 Although the underlying mechanisms are not fully understood, comorbidities and especially obesity are potential confounding factors that may in part account for the reported detrimental effects of sleep-disordered breathing on glucose regulation.5

The objective of the present study was to examine the association between GDM and maternal sleep-disordered breathing prevalence ascertained by complete polysomnography, in a cross-sectional body mass index (BMI, calculated as weight (kg)/[height (m)]2)–matched case–control design to determine the relevance of evaluating sleep-disordered breathing in pregnant women with a diagnosis of GDM. To decrease the effect of BMI, the study was conducted in women whose prepregnancy BMI were less than 35.

MATERIALS AND METHODS

Participants were recruited from February 2010 to August 2012 in the tertiary obstetrics centers of Centre hospitalier universitaire de Québec (CHU de Québec), Quebec, Canada, and McGill University Health Centre, Montreal, Quebec, Canada. Potential participants in the case and control groups were identified weekly from blood test results of pregnant women routinely screened for GDM by oral glucose tolerance tests (OGTT) between 24-0/7 and 32-6/7 weeks of gestation. For each recruited case, one women in the control group was recruited within 1 or 2 weeks. Women in the case group and those in the control group were matched for BMI±1, maternal age±5 years, and gestational age at the time of the polysomnography ±4 weeks. Inclusion criteria were age 18 years or older, singleton pregnancy, and planned delivery at one of the study centers. Exclusion criteria were diagnosis of sleep apnea, type 1 or type 2 diabetes, or chronic hypertension before pregnancy or diagnosed before 20 weeks of gestation, no follow-up, uncontrolled thyroid dysfunction, or prepregnancy BMI 35.0 or greater. The study was approved by the respective institutional research ethics boards of the CHU de Québec and the McGill University Health Centre. All participants gave written informed consent.

Cases were women with a confirmed diagnosis of GDM on routine 75-g OGTT at 24–28 weeks of gestation. Diagnosis was based on 2008 Canadian Diabetes Association criteria for 75-g OGTT and required two or more abnormal plasma glucose levels (fasting 95.5 mg/dL or greater, 1-hour 191.0 mg/dL or greater, and 2-hour 160.4 mg/dL or greater).9 Control participants were women who underwent a routine 1-hour 50-g OGTT within 1 week of case recruitment with normal plasma glucose values, ie, 1-hour less than 140.5 mg/dL.

Few polysomnography data on the frequency of sleep-disordered breathing were available in pregnant women at the time of the study. Based on a study reporting a mean apnea–hypopnea index of 5.6±5.6 respiratory events per hour in nonobese pregnant women when conservative criteria for the diagnosis of obstructive sleep apnea–hypopnea were applied,10 we estimated that a sample size of 25 patients per group would allow detection of a difference of five or greater in the mean number of events per hour between the two groups with 80% power and α of 0.05. A difference of at least five events per hour is clinically significant, because impaired glucose metabolism is usually reported for such a difference.11

A research assistant measured patients' weight and height at enrolment, obtained their demographic, medical, and obstetric data, and administered the questionnaires. Subjective sleep quality (latency, efficiency, disturbance, sleep medication, daytime function) and self-reported sleep duration over the past month were assessed with the Pittsburgh Sleep Quality Index.12 A global score of 5 or greater indicated “poor sleep.” Woman's snoring was reported by their bed partner or roommate. Snoring before pregnancy was also estimated. The frequency and duration of daytime napping (greater than 5 minutes per day) during pregnancy were also assessed by self-reporting. Daytime sleepiness was quantified by the Epworth Sleepiness Scale.13 Scores of 11 or greater indicate excessive daytime sleepiness. Fatigue was self-reported using the Fatigue Severity Scale,14 a nine-item questionnaire assessing fatigue severity and functional disability. High scores indicated excessive levels of fatigue in everyday life. Restless legs syndrome was identified using a standardized questionnaire.15 Risk for depression was self-assessed on the Edinburgh Postnatal Depression Scale with a score of at least 10 predictive of major depression.16 Finally, glycated hemoglobin was measured.

Overnight, unattended complete home polysomnography (level II) was performed within 2 weeks after enrollment. For this purpose, a portable digital monitoring device recorded standard electroencephalograms with C3/A2 and C4/A1 leads with bilateral electrooculograms, submentalis and anterior tibialis muscle electromyograms, airflow through both nasal pressure transducer and oronasal thermistor, thoracoabdominal motion on respiratory impedance plethysmography with bands around the thorax and abdomen, continuous arterial oxyhemoglobin saturation on digital pulse oximetry, continuous electrocardiograms, body position, and sound through dedicated sensors. The device was installed by a sleep technologist in the participant's home with verification of electrode impedance and signal quality. It was returned to the sleep laboratory by the participant in the morning.

Polysomnography recordings were scored at the sleep laboratory of the Institut universitaire de cardiologie et pneumologie de Québec by trained sleep technologists with Pursuit Advanced Sleep System software. Scoring was done manually according to standard sleep or wake,17 arousal,18 and respiratory19 criteria followed by expert sleep physician review. Both physicians and technologists were blinded to patients' GDM status. Apneic–hypopneic events were scored to yield the apnea–hypopnea index: apnea was defined as the absence of nasal or buccal flow for 10 seconds or greater; hypopnea was a decrease in flow amplitude greater than 50%; or a decline of 30% or greater associated with either arousal or oxygen desaturation 4% or greater (both for at least 10 seconds). The latter are the American Academy of Sleep Medicine research (Chicago) criteria,19 which were selected because they are more sensitive20 than the recommended American Academy of Sleep Medicine clinical criteria21 in use at the time of the study. Respiratory events were scored as obstructive in the face of persisting respiratory effort. Arousal was defined as electroencephalogram frequency shift to the alpha range for at least 3 seconds. Obstructive sleep apnea was considered as apnea–hypopnea index of 15 events or more per hour of sleep (moderate obstructive sleep apnea).19 Sensitivity analyses were conducted with apnea–hypopnea index of five or more events per hour. We also evaluated the percent of total sleep time spent with flow-limited breathing (plateauing of the inspiratory nasal pressure flow signal). Standard measures of sleep duration and quality were derived from polysomnography analysis.19

Data are expressed as mean±standard deviation for continuous variables or as percentages for categorical variables analyzed by χ2 or Fisher’s exact test, as appropriate (eg, restless legs syndrome, depression, sleepiness). Groups were compared by Student’s t test for continuous data. The assumption of normality was verified with the Shapiro-Wilk test. Brown and Forsythe's variation of Levene's test statistic verified the homogeneity of variances. Variables with univariate test P<.25 values were candidates for logistic and linear multivariate model building. Stepwise and backward variable selection was performed. Relationships between continuous variables were assessed by Pearson's correlation coefficient. Results were considered significant with P<.05. Data were analyzed with SAS statistical package 9.3.

RESULTS

A total of 255 participants (41 women in the case group and 214 in the control group) among 606 pregnant women (112 women in the case group and 494 in the control group) who underwent a routine test for GDM in the study period were approached and expressed interest. Women in the case group were consecutively and systematically approached for eligibility. Once a women for the case group was recruited, normoglycemic women in the control group who performed their OGTT in the same period were contacted and the first one matching a woman in the case group was recruited if interested in participating. Among eligible patients (32 women in the case group and 129 in the control group) who consented to participate, complete polysomnography results were available in 26 women in the case group and 26 matched women in the control group (Fig. 1). Participant characteristics are presented in Table 1. Women in the case group and those in the control group were similar with respect to maternal age, parity, education, smoking status, and BMI. Although statistically significant, differences in gestational age at the polysomnography and glycated hemoglobin levels were not clinically meaningful between the two groups. However, patients with GDM reported a higher risk for depressive symptoms than women in the control group (scores of 10 or greater, P=.023).

Fig. 1
Fig. 1:
Flowchart of case and control recruitment. *Four lived too far away from the research centers, one delivered prematurely, three exceeded gestational age at inclusion, and one planned to deliver outside the participating hospitals. Thirteen had a prepregnancy body mass index (BMI) greater than 35 kg/m2, 16 lived too far away from the research centers, two had a prepregnancy diagnosis of diabetes, one had prior diagnosis of sleep apnea, 31 exceeded gestational age at inclusion, one had uncontrolled hypothyroidism, one had a spontaneous abortion, one had chronic hypertension before pregnancy, one planned to deliver outside the participating hospitals, two had twin pregnancies, 11 were ineligible because physician approval to contact patients was not obtained, and five did not speak French or English. Five canceled polysomnography and one had insufficient sleep time. §Three canceled polysomnography, one delivered before polysomnography, and two had technical problems with polysomnography.Bisson. Gestational Diabetes and Sleep Apnea. Obstet Gynecol 2014.
Table 1
Table 1:
Patient Characteristics

Objective sleep parameters are reported in Table 2. Polysomnography recordings were of high quality with a low rate of signal loss (two of 55 [3.6%]). Key polysomnography variables, including total sleep time, apnea–hypopnea index (primary outcome), oxygen desaturation index, and arousal index, were similar in the two groups. The apnea–hypopnea index was higher in rapid eye movement sleep than in nonrapid eye movement sleep stages as is usually found. Based on an apnea–hypopnea index of 15 events per hour or greater, obstructive sleep apnea–hypopnea was present in only one (GDM) participant, whereas using apnea–hypopnea index of five or greater, obstructive sleep apnea–hypopnea was present in 31% of GDM and 20% of control participants (crude odds ratio [OR] 1.87, 95% confidence interval [CI] 0.52–6.73, P=nonsignificant; prepregnancy BMI-adjusted OR 1.90, 95% CI 0.52-6.88, P=nonsignificant). The percentage of sleep time spent with flow limitation was also similar in both groups.

Table 2
Table 2:
Polysomnographic Parameters

Self-reported sleep parameters are reported in Table 3. A total of 25 of 26 (96% of women in the case group) and 23 of 26 (88% of women in the control group) women had a bed partner or roommate. There were no significant differences in self-reported snoring before pregnancy in women in the case group compared with women in the control group. Snoring frequency during the past month as reported by women's bed partner or roommate was similar in the two groups with habitual snoring reported in 17% of GDM and 24% of control participants. Napping was frequent with 24 of 26 (82%) women in each group reporting naps greater than 5 minutes per day, at least once a week, with a mean duration of 58.9±36.4 minutes per day and 59.8±36.4 minutes per day in women in the case group and those in the control group, respectively. Self-reported sleep time was not different between groups. Neither recorded nor reported total sleep time values were associated with BMI or glycemia post–50-g OGTT.

Table 3
Table 3:
Self-Reported Sleep Parameters

Subjective sleep quality was similar between groups. Sleepiness score was significantly higher in women with GDM compared with normoglycemic women in the control group. Daytime sleepiness was also correlated with depressive symptoms (r=0.36, P=.009). Finally, prevalence of restless legs syndrome was high in the GDM group but was not found statistically more prevalent than in controls (crude OR 3.51, 95% CI 0.90–15.71, P=.07). Women with restless legs syndrome did not report more sleepiness (P=.43) but reported a higher score on the Edinburgh scale, suggesting an elevated risk of depressive symptoms (P<.001).

DISCUSSION

We found no significant difference in either subjective or objective sleep-disordered breathing between women with compared with without GDM. Participants reported a similar frequency of habitual snoring. Even using sensitive criteria20,21 to score respiratory events, and an apnea–hypopnea index threshold value of five or more events per hour, we found similar obstructive sleep apnea–hypopnea prevalence among GDM and control groups, respectively. Obstructive sleep apnea–hypopnea when present was generally mild without major event-related O2 desaturation in these patients without morbid obesity. We also analyzed the time spent with inspiratory flow limitation, an indicator of subtle upper airway obstruction during sleep, and found no significant difference between groups. Thus, although mild sleep-disordered breathing was present in our participants, despite our small sample size, our objective findings do not point to an association between sleep-disordered breathing and GDM.

Several factors are linked with metabolic disorders during pregnancy, including maternal obesity.22 Obesity is a major risk factor for obstructive sleep apnea–hypopnea and is related to many adverse pregnancy outcomes, confounding the association between obstructive sleep apnea–hypopnea and GDM.23 Two recent systematic reviews found that maternal sleep-disordered breathing may be associated with GDM with pooled adjusted (for obesity among other confounders) OR 1.86 (95% CI 1.29–2.37)4 and 3.06 (95% CI 1.89–4.96),5 although ORs were highly variable from one study to another and objective measures were not always performed. Considerable uncertainty exists regarding the relationship between sleep-disordered breathing symptoms compared with objectively measured obstructive sleep apnea–hypopnea with respect to pregnancy outcomes.24 Given this, in the present study, we used the gold standard of complete polysomnography for ascertainment of sleep-disordered breathing to evaluate the link between sleep-disordered breathing and GDM.

Moreover, few previous polysomnography-based studies assessed the obstructive sleep apnea–hypopnea–GDM relationship with adjustment for confounders.25–27 Chen et al25 reported a significant link of obstructive sleep apnea–hypopnea with GDM (adjusted OR 1.63, 95% CI 1.07–2.48). However, obesity was not assessed by BMI values, and polysomnography was performed within 1 year before delivery. Reutrakul et al27 recently reported that GDM was associated with a 6.6 -fold increase in the risk of objective obstructive sleep apnea (95% CI 1.15–37.96) in late pregnancy. However, their GDM and non-GDM groups were significantly different with regard to prepregnancy BMI (37.4±9.2 compared with 29.7±7.5, P=.02), highlighting the important role this variable plays in the association between sleep-disordered breathing and GDM. Indeed, Louis et al26 recently studied morbidly obese pregnant women (prepregnancy BMI greater than 30) and found no difference in GDM prevalence between those with compared with without objectively documented obstructive sleep apnea–hypopnea. Our finding that sleep-disordered breathing prevalence and severity were not greater among GDM participants without morbid obesity highlights the importance of evaluating the role of obesity in further studies aimed at assessing the mechanisms and consequences of maternal obstructive sleep apnea–hypopnea. Although a strength of this study, the exclusion of morbidly obese women constrains study generalizability to all pregnant women.

As reported among nonpregnant women,28 our data also suggest that GDM participants demonstrated significantly more subjective daytime sleepiness. However, this increased sleepiness was not explained by differences in self-reported or polysomnography-determined sleep efficiency or duration. Restless legs syndrome prevalence was high among our GDM participants. This syndrome, characterized by an irresistible urge to move the legs in sedentary situations, is associated with difficulty initiating sleep and nocturnal awakenings, and our data may suggest a high prevalence of this syndrome in the third trimester of pregnancy.29 Although restless legs syndrome scores were not correlated with sleepiness, this underscores the complexity of the interaction among GDM, restless legs syndrome, sleep duration, daytime sleepiness, and depression in pregnant women.6

In conclusion, this study did not identify a significant relationship between obstructive sleep apnea–hypopnea and GDM in women without prepregnancy morbid obesity. Based on these findings, there is no indication to recommend systematic screening by polysomnography in pregnant women with GDM free of morbid obesity or other obstetric comorbidities. However, this study did not evaluate the effects of preexisting obstructive sleep apnea–hypopnea on metabolic function. Given the potential adverse effects of sleep-disordered breathing in pregnancy, physicians should actively look for signs of obstructive sleep apnea–hypopnea in pregnant women and request objective sleep recordings in suspected cases.30

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© 2014 by The American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.