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High risk for obstructive sleep apnea and other sleep disorders among overweight and obese pregnant women

BMC Pregnancy and Childbirth volume 15, Article number: 198 (2015) Cite this article

Abstract

Background

Obstructive sleep apnea (OSA), a common and serious disorder in which breathing repeatedly stops during sleep, is associated with excess weight and obesity. Little is known about the co-occurrence of OSA among pregnant women from low and middle-income countries.

Methods

We examined the extent to which maternal pre-pregnancy overweight or obesity status are associated with high risk for OSA, poor sleep quality, and excessive daytime sleepiness in 1032 pregnant women in Lima, Peru. The Berlin questionnaire was used to identify women at high risk for OSA. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were used to examine sleep quality and excessive daytime sleepiness, respectively. Multinomial logistic regression procedures were employed to estimate odds ratios (aOR) and 95 % confidence intervals (CI) adjusted for putative confounding factors.

Results

Compared with lean women (<25 kg/m2), overweight women (25–29.9 kg/m2) had 3.69-fold higher odds of high risk for OSA (95 % CI 1.82–7.50). The corresponding aOR for obese women (≥30 kg/m2) was 13.23 (95 % CI: 6.25–28.01). Obese women, as compared with their lean counterparts had a 1.61-fold higher odds of poor sleep quality (95 % CI: 1.00–2.63).

Conclusion

Overweight or obese pregnant women have increased odds of sleep disorders, particularly OSA. OSA screening and risk management may be indicated among pregnant women in low and middle income countries, particularly those undergoing rapid epidemiologic transitions characterized by increased prevalence of excessive adult weight gain.

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Background

Obesity continues to be one of the fastest growing metabolic conditions worldwide. According to the World Health Organization (WHO) the prevalence of obesity is estimated to be approximately 12 % making it one of the 21st century epidemic disease [1]. The prevalence of obesity in Andean Latin American countries has almost doubled from 9 % in 1980 to 17 % in 2008 [2]. Recent studies conducted in Peru found that more than 40 % of the adult population in the metropolitan Lima is overweight or obese [3]. Excess consumption of energy dense foods and lack of physical activity have been implicated as reasons for the rise in obesity prevalence in Peru and elsewhere. Major obesity-related chronic disorders include: cardiovascular diseases, diabetes, hypertension, and breathing difficulties [1]. During pregnancy, maternal overweight or obese status are associated with adverse perinatal and neonatal outcomes such as gestational diabetes [4], pregnancy induced hypertension, preeclampsia [5], cesarean delivery [6], miscarriage [7], large for gestational age [4], macrosomia [8], and stillbirth [9].

There is a growing body of evidence that documents the impact of obesity on sleep disorders, more specifically obstructive sleep apnea (OSA) [2, 10, 11]. OSA is a condition characterized as repeated episodes of complete or total blockage of the upper airway during sleep [12, 13]. Snoring, persistent daytime sleepiness, and periods of awakening out of breath during the night are hallmark symptoms of OSA [13]. Epidemiologic studies have shown that OSA and poor sleep quality are independently associated with weight gain, cardiometabolic disorders, cognitive impairments, hypertension, psychiatric disorders and headaches [2, 14]. Accumulating evidence also documents a strong, positive and bi-directional association of obesity with high risk for OSA and other sleep disorders [2, 15]. Investigators have argued that increased fat deposits around the upper airways of overweight and obese individuals can produce obstruction of breathing, reducing the flow of oxygen leading to sleep apnea [16]. Conversely, OSA can contribute to obesity because fragmented and non-restorative sleep related to sleep apnea has been associated with increased caloric intake [15]. Sleep patterns are known to change throughout pregnancy in part due to physiological and hormonal changes [17]. Studies have reported increased snoring and narrower upper airways during the third trimester of pregnancy compared to postpartum [18].

Despite the high prevalence of sleep disorders among pregnant women [19] and despite a well-established body of evidence showing elevated BMI as a significant predictor of sleep disorders [20], few studies have investigated the risk of sleep disorders among overweight and obese pregnant women. To fill this void in the literature, we sought to assess the extent to which pre-gestational overweight and obesity status are associated with increased odds of high risk of OSA and other sleep disorders among pregnant women in Peru.

Methods

Participants, sample size and study setting

This study was conducted among pregnant women attending prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in the city of Lima, Peru between February 2013 and March 2014. The INMP, overseen by the Peruvian Ministry of Health, is the primary referral hospital for maternal and perinatal care. Eligible women were 18 years of age or older, could speak and read Spanish, and with a gestational age between 24 to 28 weeks. Enrolled participants were invited to participate in an interview where trained research personnel used a structured questionnaire to elicit information regarding maternal socio-demographic, lifestyle characteristics, medical and reproductive histories, and sleep characteristics. Anthropometric measures and vital signs were measured by experienced midwives. Women were weighed in light clothing using the WHO standard guidelines. All participants provided written informed consent and study procedures were approved by institutional review boards of the INMP, Lima, Peru and the Harvard T.H. Chan School of Public Health Office of Human Research Administration, Boston, MA, USA.

Instruments and variable specification

The Berlin Questionnaire originated from Berlin, Germany is a widely used and validated screening instrument for assessing high risk for obstructive sleep apnea (OSA). The questionnaire consists of 11 questions separated into three sections [21, 22]. Section 1 asked participants whether they snore. Those who responded affirmatively were asked how loud their snoring was, how often it occurred, and whether their snoring bothered other people. In the present study, participants were also asked whether anyone has ever noticed cessation of their breathing during sleep. Section 2 asked participants how often they felt tired or fatigued right after sleep, how often they felt tired, fatigued, or not up to par during wake time, and whether they ever fall asleep driving a car. In section 3, participants were asked about a history of hypertension, as well as their height, weight, and age. A section was considered positive if there were two affirmative answers in either section 1 or 2, or one affirmative response in section 3. In section 3, high risk for OSA was defined when there was a history of hypertension or obesity.

The Berlin questionnaire is widely used in pregnancy [23–26]. For the purposes of this study when 2 or more sections were classified as positive, the participant was deemed to be at high risk for OSA [21, 22]. In addition, given the lack of consensus concerning the utility of this diagnostic criteria in pregnancy [23–25], we evaluated the extent to which snoring (i.e., those positive for section 1) is associated with maternal obesity status.

Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-reported questionnaire that assesses sleep quality over the past month [27]. The PSQI has seven sleep components: sleep duration, disturbance, latency, habitual sleep efficiency, use of sleep medicine, daytime dysfunction due to sleepiness and overall quality of sleep. Each component produced a score ranging from 0 to 3, where a score of 3 indicates the highest level of dysfunction. A global sleep quality score is obtained by summing the individual component scores (range 0 to 21) with higher scores indicative of poorer sleep quality during the previous month. Participants with global scores that exceed 5 are classified as poor sleepers [27]. Those with a score of 5 or less were classified as good sleepers. This classification system is consistent with prior studies of pregnancy including those conducted in Peru [28–30].

Daytime sleepiness was measured using the Epworth Sleep Scale (ESS) [31]. The instrument has been widely validated globally including in Peru [32]. The ESS is an 8-item questionnaire capturing an individual’s propensity to fall asleep during commonly encountered situations on a scale from 0 to 3. Overall scores range between 0 and 24. ESS scores of 10 or higher are indicative of excessive daytime sleepiness [31].

Other covariates

Maternal age at the time of interview was categorized as follows: 18–20, 20–29, 30–34, and ≥35 years. Other sociodemographic variables were categorized as follows: maternal and paternal educational attainment (≤6, 7–12, and >12 completed years of schooling); marital status (married and living with partner vs. others); access to basic foods (very hard/hard, somewhat hard, not very hard); food insecurity (yes vs. no); access to medical care (very hard/hard, somewhat hard, not very hard); and parity (nulliparous vs. multiparous). Pre-pregnancy body mass index was calculated as weight (in kilograms) divided by the square of height (in meters and used to identify lean (BMI < 25 kg/m2), overweight (BMI: 25–29.9 kg/m2), and obese (BMI ≥ 30 kg/m2) women according to the World Health Organization (WHO) criteria.

Statistical analysis

We examined the distributions of maternal sociodemographic, reproductive, and medical characteristics according to pre-pregnancy BMI categories. Multivariate multinomial logistic regression models were fitted to estimate adjusted odds ratios [33] and 95 % confidence intervals (95 % CI) of sleep disorders in relation to maternal overweight and obesity status after adjusting for potential confounders. Separate models were fitted for each sleep complaint. In multivariable models we adjusted for maternal age, educational attainment, marital status, and parity. Additional adjustment for the other covariates listed in Table 1 did not substantially change odds ratios. We explored the possibility of a nonlinear relation between BMI and odds of high risk for OSA, using generalized additive logistic regression modeling procedures (GAM). Finally, on the basis of study findings reporting limited clinical utility of the total score of the Berlin questionnaire in pregnancy [25], we conducted post-hoc analyses to examine the extent to which maternal overweight and obesity status are associated with increased odds of snoring (as assessed using information from section 1 of the Berlin questionnaire). All analyses were performed using Stata 12.0 statistical software (Stata, College Station, TX). The GAM analyses were performed using “R” software version 3.1.2. All reported p-values are two-tailed and deemed statistically significant at α = 0.05.

Table 1 Maternal socio-demographics characteristics
Full size table

Results

The socio-demographic characteristics of the participants are presented in Table 1. A total of 1032 pregnant women between the ages of 18 and 45 years (mean age = 28.6 years, standard deviation = 6.2 years) participated in the study. The majority of participants were married or living with their partner (87.6 %) while more than half (55.5 %) reported an education attainment of 7 or more years. Approximately 50 % of participants reported having difficulty paying for basics (48.9 %), while 64.2 % reported food insecurities and more than three fourth (88.6 %) reported difficulties to accessing medical care.

Table 2 shows the relationship between pre-pregnancy BMI and sleep disorders. The prevalence of high risk for OSA was 2.1 %, 8.0 % and 25.7 % for lean, overweight and obese study participants, respectively. The corresponding prevalence for poor sleep quality were 19.0 %, 21.4 %, and 28.4 %. Generally similar prevalence of excessive daytime sleepiness was noted among lean (12.7 %), overweight (13.1 %) and obese (12.9 %) study participants. In general, as pre-pregnancy BMI increased, the odds of sleep disorders increased. After adjusting for confounders compared with normal weight women (<25 kg/m2), overweight women (25–29.9 kg/m2) had 3.69-fold higher odds of experiencing high risk for OSA (assessed using the Berlin questionnaire) (95 % CI: 1.82–7.50). Obese women (≥30 kg/m2) had a 13.2-fold higher odds of experiencing high risk for OSA (aOR = 13.23; 95 % CI: 6.25–28.01) as compared with their lean counterparts. Additionally the odds of high risk for OSA was modeled in relation to BMI expressed as continuous variable using procedures based on a general additive model. Results from these analyses confirmed a linear relationship between increasing BMI and the odds of high risk for OSA (Fig. 1).

Table 2 Odds of obstructive sleep apnea, poor sleep quality, and excessive daytime sleepiness in relation to pre-pregnancy body mass index (BMI)
Full size table
Fig. 1
figure 1

Relation between log odds of high risk for obstructive sleep apnea and pre-pregnancy body mass index (solid line) with 95 % confidence interval (dotted lines)

Full size image

Compared with lean women, overweight women were associated with modest elevated, and statistically non-significant, odds of poor sleep quality (aOR = 1.16; 95 % CI: 0.82–1.63). However, obese women had a statistically significant 1.61-fold increased odds of poor sleep quality (95 % CI: 1.00–2.63), as compared with lean women (Table 2). We observed no clear evidence of an association of pre-pregnancy BMI and excessive daytime sleepiness. In post-hoc analyses restricted to the snoring components of the Berlin questionnaire (section 1), we found that compared with lean women, obese (aOR = 1.83; 95 % CI: 1.03–3.24) and overweight women (aOR = 1.20; 95 %CI: 0.78–1.83) were more likely to report snoring during pregnancy.

Discussion

Overall, we found that pregnant women who are overweight and obese have increased odds of sleep disorders. Compared with lean women (<25 kg/m2), overweight women (25–29.9 kg/m2) had 3.69-fold higher odds of high risk for OSA (95 % CI 1.82–7.50). The corresponding OR for obese women (≥30 kg/m2) was 13.23 (95 % CI: 6.25–28.01). Obese women, as compared with their lean counterparts had a 1.61-fold higher odds of poor sleep quality (95 % CI: 1.00–2.63).

In the present study, the prevalence estimates of high risk for OSA were 6.5 % (assessed using Berlin questionnaire). The prevalence of high risk for OSA found in our study is lower than estimates from other studies. In their study among predominantly Hispanic pregnant women in Houston, Texas Antony et al. found a 15.5 % prevalence of high risk for OSA assessed using Berlin questionnaire [4]. The investigators further noted that obesity was associated with a 9-fold increased odds (95 % CI 4.68–17.39) of high risk for OSA compared with normal weight women in that population. In a study of 276 pregnant women in Korea, Ko et al. [20] found a high prevalence of OSA in obese women (43.6 %) compared with non-obese women (32.6 %) (p = 0.001). Their findings indicating increased odds of high risk for OSA among overweight and obese pregnant women are in general agreement with our study findings. Other studies conducted among men and non-pregnant women [34–36] report findings that are in agreement with those reported by our team and others [4, 20]. For example, using data from the 2007 Sleep in America Poll of the National Sleep Foundation, Kapsimalis and Kryger [37] noted that the prevalence of high risk for OSA (determined using Berlin questionnaire) was 8.5 % among women with normal BMI while the prevalence estimates were markedly higher among overweight (21 %) and obese (62 %) women. In a recently published study of college students in Chile, Wosu et al. [15] reported, that obese students were 8.26 times-as likely to experience high risk for OSA compared with normal weight students (95 % CI:4.59–14.86) after adjusting for confounders. Another finding that merits consideration is our results that showed obese pregnant women were 1.83-time as likely to report snoring (aOR = 1.83; 95 % CI: 1.03–3.24) during pregnancy as compared with lean women. However, this association did not reach statistical significance for overweight women (aOR = 1.20; 95 % CI: 0.78–1.83). Investigators have documented similar findings and have speculated that pregnant women are more likely to have higher BMI while carrying a lesser proportion of body fat and therefore more likely to meet the criteria of obesity used by the Berlin questionnaire [23]. Taken together, the current data indicate that the Berlin questionnaire might have limited utility in OSA screening due to the inclusion of obesity as one of the classification criteria.

In the last 50 years, the average self-reported sleep duration in the United States has decreased by 1.5 to 2 h while the prevalence estimates of obesity and diabetes have increased with Hispanics and Blacks showing marked increase [38, 39]. A large literature primarily focused on men and non-pregnant women has shown that obesity is related to sleep insufficiency and poor sleep quality [40]. For instance, Chaput et al. (2007) in Canada found that women with short sleep duration (5 to 6 h) were 1.69-times as likely (OR = 1.69; 95 % CI: 1.15–2.39) to be overweight or obese compared with normal sleepers (7 to 8 h) [41]. In Taiwan Hung et al. (2012) noted that being overweight or obese was statistically significantly associated with increased global PSQI scores (p < 0.001) [40]. Logue et al. (2014) in an urban family medicine center in the US reported a statistically significant association of poor sleep quality and obesity (p = 0.005) independent of age, gender, and ethnicity [42]. It is well established that sleep is altered during pregnancy [43]. Of note, in the second and third trimesters, pregnant women are more likely to have frequent awakenings due to fetal movements, discomfort, backaches as well as frequent urge to urinate due to an enlarged uterus [44] contributing to sleep insufficiency and fragmentation. Poor material sleep quality and other sleep disorders influence not only the mother but also their offspring to adverse cardio metabolic pathology later in life.

In the current study, we found no evidence of an association between BMI and excessive daytime sleepiness after adjustment for possible confounders. Dixon et al. (2007), in their study among 1055 Australian patients presenting for obesity surgery, also found no statistically significant association between ESS scores and BMI [45]. We do not have an explanation for these null findings although it is important to note that the ESS is a global summary score of questions on the risk of daytime sleepiness during different situations during the daytime. The ESS was originally developed to measure one construct—excessive daytime sleepiness [27, 31]. However, an emerging literature has shown that the eight items of ESS do not necessarily assess a unidimensional construct rather two or three different aspects of daytime sleepiness [46]. Hence, the summary score of the all eight items may not be the best index of excessive day time sleepiness. Future studies should look at how individual items might be influenced by obesity/overweight status.

Several plausible and compelling biological mechanisms have been proposed to explain the observed obesity/overweight-sleep disorder associations. Obesity can contribute to many physiological changes that increase the risk of OSA. For example, excessive soft tissue due to obesity, may narrow the pharyngeal airway and reduce lung volume [2]. Additionally, pregnancy induces many physiological changes. These includes enlargement of the uterus which can elevate the diaphragm and alter respiration. These alterations, for instance, may increase the tendency for collapsing the upper airway during sleep [19]. Changes in hormones also predispose pregnant women to episodes of sleep apnea. Notably, increased estrogen concentration has been linked to mucosal edema, and progesterone has been linked to increase respiratory center sensitivity to CO2, therefore causing instability of the respiratory control mechanism [47]. Collectively, these physiological and hormonal changes may interact to increase the obesity-sleep disorders associations.

Our study has several notable strengths including a relatively large sample size, the use of cross-culturally accepted instruments to characterize sleep disorders and rigorously trained research staff administering the questionnaires. However, several important limitations must be considered when interpreting our findings. First, since this was a cross-sectional study, we cannot delineate the temporal relationship between maternal elevated BMI and sleep disorders. We also cannot determine whether sleep disturbances may be attributed to pregnancy related physiological alterations. Second, our findings are based on a population of pregnant women seeking care at a specialized maternal hospital; hence, caution is needed before generalizing the results to other populations. Third, although we used multiple validated questionnaires, these questionnaires may have contributed to some errors in the classification of participants’ sleep disturbances [20]. Use of screening questionnaires for assessing sleep disorders in pregnancy is an important component of sleep health research. The success of screening however, is largely dependent on the accuracy of the questionnaires and use diagnostic procedures, used as the gold standard, to evaluate the effectiveness of screening questionnaires. The gold standard for documenting many sleep disorders including OSA is in-laboratory polysomnography (PSG) assessment. Unfortunately due to cost, complexity and participant burden, use of PSG testing is limited in large-scale epidemiologic studies. Thus use of screening questionnaires with low specificity remains the main modality for ascertaining OSA. Future studies are warranted to develop, refine and enhance the psychometric properties of screening questionnaires and improve their utility for early identification of sleep disorders in pregnancy. The fact that our findings are similar to those that used more invasive, though objective measures of sleep traits (e.g., polysomnography) [48] serve to attenuate some concerns.

Available literature suggests that maternal OSA may be associated with an increased risk of adverse perinatal outcomes [47, 49]. For instance in a meta-analysis of 9795 participants Luque-Fernandez et al. [49] found women with sleep disordered breathing had a 3-fold increased risk of gestational diabetes (OR = 3.06; 95 % CI: 1.89–4.96). Chen et al. [50] found that mothers with OSA were more likely to have low birth weight, preterm birth, and small for gestational age newborns, cesarean section, preeclampsia, gestational diabetes, and gestational hypertension as compared with unaffected mothers. Louis et al. [51] reported that OSA was associated with an increased risk of preterm delivery and maternal morbidity. Additionally, Kamysheva et al. have reported increased odds of antepartum and postpartum depression among women with symptoms of poor sleep [52].

Conclusion

In conclusion, we observed increased risks of sleep disorders amongst overweight and obese pregnant Peruvian women. These observations, when coupled with earlier report, have important clinical and public health implications because pregnant women with symptoms of OSA are at higher risk of adverse pregnancy and perinatal outcomes [47]. Collectively, our findings and those of others [14] underscore the clinical and public health implications for OSA screening and treatment among reproductive age and pregnant women in low and middle income countries, particularly those undergoing rapid epidemiologic transitions characterized by increased prevalence of excessive adult weight gain.

References

  1. Unwin N, Whiting D, Guariguata L, Ghyoot G, and Gan D, Eds., Diabetes Atlas, International Diabetes Federation, Brussels, Belgium, 5th edition, 2011.

  2. Burke CCILE. The obesity epidemic: the USA as a cautionary tale for the rest of the world. Curr Epidemiol Rep. 2014;1:82–8.

    PubMed  PubMed Central  Google Scholar 

  3. Ortiz DM: More than 40 percent of Peruvian adults are obese or overweight. In: National. Peru; 2013.

  4. Carreno CA, Clifton RG, Hauth JC, Myatt L, Roberts JM, Spong CY, et al. Excessive early gestational weight gain and risk of gestational diabetes mellitus in nulliparous women. Obstet Gynecol. 2012;119(6):1227–33.

    PubMed  PubMed Central  Google Scholar 

  5. Chandrasekaran S, Levine LD, Durnwald CP, Elovitz MA, Srinivas SK. Excessive weight gain and hypertensive disorders of pregnancy in the obese patient. J Matern Fetal Neonatal Med. 2014;17:1–5.

    Google Scholar 

  6. Weiss JL, Malone FD, Emig D, Ball RH, Nyberg DA, Comstock CH, et al. Obesity, obstetric complications and cesarean delivery rate--a population-based screening study. Am J Obstet Gynecol. 2004;190(4):1091–7.

    PubMed  Google Scholar 

  7. Boots C, Stephenson MD. Does obesity increase the risk of miscarriage in spontaneous conception: a systematic review. Semin Reprod Med. 2011;29(6):507–13.

    PubMed  Google Scholar 

  8. Egan AM, Dennedy MC, Al-Ramli W, Heerey A, Avalos G, Dunne F. ATLANTIC-DIP: excessive gestational weight gain and pregnancy outcomes in women with gestational or pregestational diabetes mellitus. J Clin Endocrinol Metab. 2014;99(1):212–9.

    CAS  PubMed  Google Scholar 

  9. Chu SY, Kim SY, Lau J, Schmid CH, Dietz PM, Callaghan WM, et al. Maternal obesity and risk of stillbirth: a metaanalysis. Am J Obstet Gynecol. 2007;197(3):223–8.

    PubMed  Google Scholar 

  10. Kohler M. Risk factors and treatment for obstructive sleep apnea amongst obese children and adults. Curr Opin Allergy Clin Immunol. 2009;9(1):4–9.

    PubMed  Google Scholar 

  11. Pien GW, Pack AI, Jackson N, Maislin G, Macones GA, Schwab RJ. Risk factors for sleep-disordered breathing in pregnancy. Thorax. 2014;69(4):371–7.

    PubMed  Google Scholar 

  12. Edwards N, Middleton PG, Blyton DM, Sullivan CE. Sleep disordered breathing and pregnancy. Thorax. 2002;57(6):555–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cutler MJ, Hamdan AL, Hamdan MH, Ramaswamy K, Smith ML. Sleep apnea: from the nose to the heart. J Am Board Fam Pract. 2002;15(2):128–41.

    PubMed  Google Scholar 

  14. Hiestand DM, Britz P, Goldman M, Phillips B. Prevalence of symptoms and risk of sleep apnea in the US population: results from the national sleep foundation sleep in America 2005 poll. Chest. 2006;130(3):780–6.

    PubMed  Google Scholar 

  15. Wosu AC, Velez JC, Barbosa C, Andrade A, Frye M, Chen X, et al. The relationship between high risk for obstructive sleep apnea and general and central obesity: findings from a sample of Chilean college students. ISRN Obes. 2014;2014:871681.

    PubMed  PubMed Central  Google Scholar 

  16. Venkata C, Venkateshiah SB. Sleep-disordered breathing during pregnancy. J Am Board Fam Med. 2009;22(2):158–68.

  17. Fung AM, Wilson DL, Barnes M, Walker SP. Obstructive sleep apnea and pregnancy: the effect on perinatal outcomes. J Perinatol. 2012;32(6):399–406.

    CAS  PubMed  Google Scholar 

  18. Izci B, Vennelle M, Liston WA, Dundas KC, Calder AA, Douglas NJ. Sleep-disordered breathing and upper airway size in pregnancy and post-partum. Eur Respir J. 2006;27(2):321–7.

    CAS  PubMed  Google Scholar 

  19. Cai XH, Xie YP, Li XC, Qu WL, Li T, Wang HX, et al. The prevalence and associated risk factors of sleep disorder-related symptoms in pregnant women in China. Sleep Breath. 2013;17(3):951–6.

    PubMed  Google Scholar 

  20. Ko HS, Kim MY, Kim YH, Lee J, Park YG, Moon HB, et al. Obstructive sleep apnea screening and perinatal outcomes in Korean pregnant women. Arch Gynecol Obstet. 2013;287(3):429–33.

    PubMed  Google Scholar 

  21. Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med. 1999;131(7):485–91.

    CAS  PubMed  Google Scholar 

  22. Meerlo P, Sgoifo A, Suchecki D. Restricted and disrupted sleep: effects on autonomic function, neuroendocrine stress systems and stress responsivity. Sleep Medicine Reviews. 2008;12(3):197–210.

    PubMed  Google Scholar 

  23. Wilson DL, Walker SP, Fung AM, O’Donoghue F, Barnes M, Howard M. Can we predict sleep-disordered breathing in pregnancy? The clinical utility of symptoms. J Sleep Res. 2013;22(6):670–8.

    PubMed  Google Scholar 

  24. Tantrakul V, Sirijanchune P, Panburana P, Pengjam J, Suwansathit W, Boonsarngsuk V, et al. Screening of obstructive sleep apnea during pregnancy: differences in predictive values of questionnaires across trimesters. J Clin Sleep Med. 2015;11(2):157–63.

    PubMed  PubMed Central  Google Scholar 

  25. Olivarez SA, Ferres M, Antony K, Mattewal A, Maheshwari B, Sangi-Haghpeykar H, et al. Obstructive sleep apnea screening in pregnancy, perinatal outcomes, and impact of maternal obesity. Am J Perinatol. 2011;28(8):651–8.

    PubMed  Google Scholar 

  26. Mindell JA, Cook RA, Nikolovski J. Sleep patterns and sleep disturbances across pregnancy. Sleep Medicine. 2015;16(4):483–8.

    PubMed  Google Scholar 

  27. Buysse DJ, Reynolds 3rd CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    CAS  PubMed  Google Scholar 

  28. Gelaye B, Barrios YV, Zhong QY, Rondon MB, Borba CP, Sanchez SE, et al. Association of poor subjective sleep quality with suicidal ideation among pregnant Peruvian women. Gen Hosp Psychiatry. 2015. doi:10.1016/j.genhosppsych.2015.04.014.

    PubMed  PubMed Central  Google Scholar 

  29. Okun ML, Hanusa BH, Hall M, Wisner KL. Sleep complaints in late pregnancy and the recurrence of postpartum depression. Behav Sleep Med. 2009;7(2):106–17.

    PubMed  PubMed Central  Google Scholar 

  30. Zhong QY, Gelaye B, Sanchez SE, Williams MA. Psychometric Properties of the Pittsburgh Sleep Quality Index (PSQI) in a Cohort of Peruvian Pregnant Women. J Clin Sleep Med. 2015. [Epub ahead of print].

  31. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540–5.

    CAS  PubMed  Google Scholar 

  32. Rose D, Gelaye B, Sanchez S, Castaneda B, Sanchez E, Yanez ND, Williams MA. Morningness/eveningness chronotype, poor sleep quality, and daytime sleepiness in relation to common mental disorders among Peruvian college students. Psychol Health Med. 2014:1–8. In press.

  33. Shimizu E, Hashimoto K, Okamura N, Koike K, Komatsu N, Kumakiri C, et al. Alterations of serum levels of brain-derived neurotrophic factor (BDNF) in depressed patients with or without antidepressants. Biol Psychiatry. 2003;54(1):70–5.

    CAS  PubMed  Google Scholar 

  34. Palla A, Digiorgio M, Carpene N, Rossi G, D’Amico I, Santini F, et al. Sleep apnea in morbidly obese patients: prevalence and clinical predictivity. Respiration. 2009;78(2):134–40.

    PubMed  Google Scholar 

  35. Resta O, Foschino-Barbaro MP, Legari G, Talamo S, Bonfitto P, Palumbo A, et al. Sleep-related breathing disorders, loud snoring and excessive daytime sleepiness in obese subjects. Int J Obes Relat Metab Disord. 2001;25(5):669–75.

    CAS  PubMed  Google Scholar 

  36. Mallory Jr GB, Fiser DH, Jackson R. Sleep-associated breathing disorders in morbidly obese children and adolescents. J Pediatr. 1989;115(6):892–7.

    PubMed  Google Scholar 

  37. Kapsimalis F, Kryger M. Sleep breathing disorders in the U.S. female population. J Womens Health. 2009;18(8):1211–9.

    Google Scholar 

  38. Lucassen EA, Rother KI, Cizza G. Interacting epidemics? Sleep curtailment, insulin resistance, and obesity. Ann N Y Acad Sci. 2012;1264:110–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–7.

    PubMed  Google Scholar 

  40. Hung HC, Yang YC, Ou HY, Wu JS, Lu FH, Chang CJ. The Association Between Self-Reported Sleep Quality and Overweight in A Chinese Population. Obesity. 2013;21(3):486–92.

  41. Chaput JP, Despres JP, Bouchard C, Tremblay A. Short sleep duration is associated with reduced leptin levels and increased adiposity: Results from the Quebec family study. Obesity. 2007;15(1):253–61.

    CAS  PubMed  Google Scholar 

  42. Logue EE, Scott ED, Palmieri PA, Dudley P. Sleep duration, quality, or stability and obesity in an urban family medicine center. J Clin Sleep Med. 2014;10(2):177–82.

    PubMed  PubMed Central  Google Scholar 

  43. Lee KA. Alterations in sleep during pregnancy and postpartum: a review of 30 years of research. Sleep Medicine Reviews. 1998;2(4):231–42.

    CAS  PubMed  Google Scholar 

  44. Kennelly MM, Fallon A, Farah N, Stuart B, Turner MJ. Effects of body mass index on sleep patterns during pregnancy. J Obstet Gynaecol. 2011;31(2):125–7.

    CAS  PubMed  Google Scholar 

  45. Dixon JB, Schachter LM, O’Brien PE. Sleep disturbance and obesity: changes following surgically induced weight loss. Arch Intern Med. 2001;161(1):102–6.

    CAS  PubMed  Google Scholar 

  46. Gelaye B, Lohsoonthorn V, Lertmaharit S, Pensuksan WC, Sanchez SE, Lemma S, et al. Construct validity and factor structure of the Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale in a multi-national study of African, South East Asian and South American college students. PloS One. 2014;9(12):e116383.

    PubMed  PubMed Central  Google Scholar 

  47. Venkata C, Venkateshiah SB. Sleep-disordered breathing during pregnancy. J Am Board Fam Med. 2009;22(2):158–68.

    PubMed  Google Scholar 

  48. Moraes W, Poyares D, Zalcman I, de Mello MT, Bittencourt LR, Santos-Silva R, et al. Association between body mass index and sleep duration assessed by objective methods in a representative sample of the adult population. Sleep Medicine. 2013;14(4):312–8.

    CAS  PubMed  Google Scholar 

  49. Luque-Fernandez MA, Bain PA, Gelaye B, Redline S, Williams MA. Sleep-disordered breathing and gestational diabetes mellitus: a meta-analysis of 9795 participants enrolled in epidemiological observational studies. Diabetes Care. 2013;36(10):3353–60.

    PubMed  PubMed Central  Google Scholar 

  50. Chen YH, Kang JH, Lin CC, Wang IT, Keller JJ, Lin HC. Obstructive sleep apnea and the risk of adverse pregnancy outcomes. Am J Obstet Gynecol. 2012;206(2):136. e131-135.

    PubMed  Google Scholar 

  51. Louis JM, Auckley D, Sokol RJ, Mercer BM. Maternal and neonatal morbidities associated with obstructive sleep apnea complicating pregnancy. Am J Obstet Gynecol. 2010;202(3):261. e261-265.

    PubMed  Google Scholar 

  52. Kamysheva E, Skouteris H, Wertheim EH, Paxton SJ, Milgrom J. A prospective investigation of the relationships among sleep quality, physical symptoms, and depressive symptoms during pregnancy. J Affect Disord. 2010;123(1–3):317–20.

    PubMed  Google Scholar 

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Acknowledgment

This research was supported by Roche Diagnostic Operations Inc. (project number 208617–5074547) and the National Institutes of Health (NIH), National Institute for Minority Health and Health Disparities (T37-MD000149). The funders had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. The authors wish to thank the dedicated staff members of Asociacion Civil Proyectos en Salud (PROESA), Peru and Instituto Especializado Materno Perinatal, Peru for their expert technical assistance with this research.

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Authors and Affiliations

  1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, K501, Boston, MA,, 02115, USA

    Jayne R. Rice, Miguel Angel Luque Fernandez, Yasmin V. Barrios, Bizu Gelaye & Michelle A. Williams

  2. Instituto Nacional Materno Perinatal de Lima, Lima, Peru

    Gloria T. Larrabure-Torrealva & Vicky Motta

  3. Departamentos de Medicina y Ginecología y Obstetricia Universidad Nacional Universidad Nacional Mayor de San Marcos, Lima, Peru

    Gloria T. Larrabure-Torrealva

  4. Universidad de Ciencias Aplicadas, Lima, Peru

    Sixto Sanchez

  5. Asociación Civil de Proyectos en Salud, AC.PROESA, Lima, Peru

    Mirtha Grande & Sixto Sanchez

Authors
  1. Jayne R. Rice
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  2. Gloria T. Larrabure-Torrealva
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  3. Miguel Angel Luque Fernandez
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  4. Mirtha Grande
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  5. Vicky Motta
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  6. Yasmin V. Barrios
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  7. Sixto Sanchez
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  8. Bizu Gelaye
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  9. Michelle A. Williams
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Corresponding author

Correspondence to Bizu Gelaye.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

MAW conceived and designed the study. JRR, MALF, BG and MAW analyzed data. JRR, BG and MAW drafted the manuscript. All authors interpreted the data, critically revised the draft for important intellectual content, and gave final approval of the manuscript to be published.

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Rice, J.R., Larrabure-Torrealva, G.T., Fernandez, M.A.L. et al. High risk for obstructive sleep apnea and other sleep disorders among overweight and obese pregnant women. BMC Pregnancy Childbirth 15, 198 (2015). https://doi.org/10.1186/s12884-015-0633-x

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Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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