|Year : 2020 | Volume
| Issue : 3 | Page : 210-219
Snoring is associated with obesity among middle aged Slum–dwelling women in Mysore, India
Karl Krupp1, Meredith Wilcox2, Arun Srinivas3, Vijaya Srinivas4, Purnima Madhivanan5, Elena Bastida6
1 Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA; Public Health Research Institute of India, Mysore, Karnataka, India
2 Midwest Biomedical Research/Center for Metabolic and Cardiovascular Health, Addison, IL; MB Clinical Research, Boca Raton, FL, USA
3 Department of Cardiology, Apollo Hospital, Mysore, Karnataka, India
4 Public Health Research Institute of India, Mysore, Karnataka, India
5 Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health; Public Health Research Institute of India, Mysore, Karnataka, India; Division of Infectious Diseases, College of Medicine; Department of Family and Community Medicine, College of Medicine, University of Arizona, Tucson, Arizona, USA
6 Department of Health Promotion and Disease Prevention, Stempel College of Public Health, Florida International University, Miami, Florida, USA
|Date of Submission||20-Nov-2019|
|Date of Acceptance||20-Jan-2020|
|Date of Web Publication||04-May-2020|
Dr. Karl Krupp
Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Avenue, P.O. Box 245209, Tucson, AZ 85724-5209
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Globally, rates of obesity have trebled in the past four decades. India has more than 9.8 million men and 20 million women classified as obese. While poor diet and sedentary lifestyles are major causes, growing evidence suggests other factors like sleep-disordered-breathing may also be contributors. Methods: A cross-sectional survey was carried out between October 2017 and May 2018 among a nonprobability sample of slum-dwelling women, 40–64 years of age, in government-designated slums in Mysore, India. After the informed consent process, data were collected on sociodemographics, tobacco and alcohol consumption, diet, physical activity, sleep, quality of life, and personal and family history of diagnosed cardiometabolic disorders. Body mass index (BMI) was calculated using anthropometry. The serum was tested for HbA1c and lipid profile. Results: In this sample of slum-dwelling women, snoring was associated with obesity. Habitual snorers had more than double the odds (adjusted odds ratio [aOR] 2.05; 95% confidence interval [CI] 1.26–3.33;P < 0.004) of obesity I, and seven times the odds (aOR 7.71; CI: 3.58–16.62;P < 0.001) of being in the obesity II category compared to nonsnorers after adjustment for age, diabetes, hypertension, hypercholesterolemia, and daytime sleepiness. There was no difference in obesity status among participants reporting abnormal sleep duration, napping, daytime sleepiness, sleep apnea, insomnia, or the use of sleep medication. Conclusion: The relationship between snoring and obesity has not been well explored. This study among slum-dwelling Indian women found a significant relationship between snoring and obesity. Future research should explore the underlying mechanisms connecting snoring to BMI.
Keywords: Body mass index, India, obesity, snoring, women
|How to cite this article:|
Krupp K, Wilcox M, Srinivas A, Srinivas V, Madhivanan P, Bastida E. Snoring is associated with obesity among middle aged Slum–dwelling women in Mysore, India. Lung India 2020;37:210-9
|How to cite this URL:|
Krupp K, Wilcox M, Srinivas A, Srinivas V, Madhivanan P, Bastida E. Snoring is associated with obesity among middle aged Slum–dwelling women in Mysore, India. Lung India [serial online] 2020 [cited 2020 Oct 1];37:210-9. Available from: http://www.lungindia.com/text.asp?2020/37/3/210/283750
| Introduction|| |
Globally, rates of overweight and obesity have trebled in the past four decades. In 2016, almost two billion adults were classified as overweight, and 650 million, as obese. Between 1975 and 2014, the share of adults that were overweight or having a body mass index (BMI) ≥25, increased from 40.8% to 69.1%. During the same period, age-standardized obesity (BMI ≥ 30 kg/m) increased from 3.2% to 10.8% in men; and 6.4% to 14.9% in women. The annual public health cost of obesity is estimated at 3.4 million premature deaths and 4% of both potential years of life lost and disability-adjusted life years worldwide.
Once considered mainly a problem in high-income nations, obesity is now a major public health issue in India. In 1975, for example, about 400,000 men and 800,000 women were obese; by 2014, that had increased to 9.8 million and 20 million, respectively. Across this country of more than a billion people, high rates of being overweight are more frequently associated with age, region, female gender, and being in an urban area as compared to rural ones. Studies suggest that rates among south Indian women are particularly high; one study showed that women living in south Indian states were about 50% more likely to be overweight or obese compared to their peers in northern India.
In addition to substantial evidence that an increasingly sedentary lifestyle and a nutritional transition to processed foods and high-calorie diets are contributing to weight gain globally, a growing body of research also suggests that disordered sleep may also be contributing to obesity. Research suggests a strong link between sleep disorders for instance and metabolic dysfunctions like insulin resistance that have been associated with obesity. Habitual snoring (HS), a sleep-related behavior, has also been associated with glucose and insulin metabolism and obesity. This relationship appears to be bi-directional. On the one hand, obesity and neck circumference have been shown to increase the risk for snoring: Disordered breathing increases negative intraluminal pressure encouraging the collapse of the airway and vibration of pharyngeal soft tissues. In some studies, obesity appeared to be causal in snoring, but this has been increasingly questioned as further research showed that weight reduction had very modest effects on snoring., Other research however, suggests that HS may also be linked to weight gain by contributing to metabolic load. HS appears to increase risk factors associated with obesity including sleep fragmentation, impaired circulatory function, hypertension, metabolic syndrome, diabetes, and impaired glucose control.,,
The alarming increase in sleep disorders and obesity have led some to describe both as epidemic, but their relationship to each other has not been well characterized. While it is well established that obesity contributes to the development and severity of obstructive sleep apnea (OSA), a growing body of evidence suggests that sleep disorders increase risk for obesity., The impact of snoring on obesity, with and without OSA, is even less well understood, but it has been associated with metabolic syndrome, weight gain in pregnant women, hypertension, kidney disease, nocturia, and obesity. This is the first study to our knowledge to explore the prevalence of snoring in slum-dwellers; its relationship to obesity; and interactions between snoring and other sleep parameters that may increase the risk for both obesity and CVD.
| Methods|| |
A cross-sectional study was carried out between October 2017 and May 2018 among a nonprobability sample of slum-dwelling women in Mysore, India. To be included, women had to be 40–64 years of age, living in a government-designated slum for a minimum of 6 months, and willing to undergo all the study procedures. Women were excluded if they had hemophilia or other medical conditions that put them at risk during sample collection. The research measured knowledge and beliefs about coronary heart disease (CHD), demographics, modifiable risk factors (smoking, use of alcohol, weight, physical activity, healthy diet, blood pressure, serum cholesterol, and blood glucose), and correlates of CHD (defined as previously diagnosed CHD, symptoms on the Rose angina questionnaire, and/or ischemic changes on electrocardiography).
The study was reviewed and approved by the institutional review boards at Florida International University in the US and Public Health Research Institute of India in India.
The study was carried out in six urban slums (Kesere, Kudaremala, Ekalavya Nagara, Amrutha badavane, KN Pura and Ganeshnagar) in Mysore City, India. The communities were randomly selected from a sampling frame of 63 Mysore communities designated as slums by the Karnataka Slum Development Board. According to the 2011 census, Mysore City has a population of 920,000, of which 493,762 are females. Approximately 19% of the population live below the poverty line, and about 39,029 residents reside in slums as defined by the Karnataka Slum Act.
Trained research staff visited study sites 1 day before recruitment and distributed brochures describing the study. If residents expressed interest, staff members explained the purpose of the research, described study activities and conducted a screening process using a standardized script. If potential participants declined to participate, they were asked a brief set of demographic questions to assess any potential systematic biases in participant recruitment.
Interested potential participants were also asked to bring any medical reports or medications for diabetes, high blood pressure, heart disease, or stroke to a pickup point the next day and were transported by van to the study site. Before data collection, potential participants underwent an informed consent process. Study staff explained the study purpose, read the informed consent verbatim, described all study procedures, and solicited and answered any questions about the study. Women were asked if they understood what they were consenting to, and whether they had any further questions. After any doubts were clarified, participants were required to give written informed consent before data collection.
Data were collected from participants in Kannada using an interviewer-administered standardized questionnaire adapted from the Centre for Cardiometabolic Risk Reduction in South-Asia (CARRS) surveillance study. Questions solicited information on demographics, socioeconomic status, employment, and residence. Data were also collected on knowledge about cardiovascular disease, willingness to adopt heart healthy behaviors, subjective judgment of general health, tobacco and alcohol consumption, dietary habits, physical activity, sleep, quality of life, and personal and family history of diagnosed cardiometabolic disorders and their risk factors. This included diabetes, heart disease, stroke, chronic obstructive pulmonary disease, angina, peripheral vascular disease, kidney disease, and respiratory diseases. Sleep duration and quality were measured using scales adapted from the National Heart Lung Blood Institute (NHLBI) Sleep Habits Questionnaire. Following data collection, each participant was individually counseled on reducing risk for cardiometabolic diseases and given a brochure outlining how they could alter their existing CHD risk.
Definition of study variables
The primary outcome of this analysis was BMI, calculated as weight in kilograms divided by height in meters squared. BMI was categorized based on the World Health Organization's recommended BMI cutoffs for South Asians. Due to small cell sizes, underweight and normal categories were merged and used as a reference.
The main exposure variable for the study was “frequency of snoring.” Categories were based on prior studies with participants defined as “nonsnorers” if they never snored; as “moderate snorers” if they currently snored 1–2 nights per week; and as “habitual snorers” if they currently snored 3–7 nights per week.
Covariates were selected based on the literature. Diabetes, hypertension, hypercholesterolemia (HTC), and anxiety/depression; sleep duration; daytime sleepiness; and frequency of napping, insomnia, and interrupted breathing while sleeping were also included in the analysis. Education was defined as no schooling, primary school (1–7 years), high school (8–12 years), and secondary school or above (12 + years). Marital status was defined as single, married, and “other.” Caffeine consumption was defined as “any” consumption of coffee or tea. Physical activity was categorized into levels based on calculated metabolic equivalent (MET)-minutes (met-mins). Diabetes was defined as estimated average glucose >125 mg/dL or taking medication for diabetes. Hypertension was defined as having a systolic blood pressure ≥130 mmHg, diastolic blood pressure ≥80 mmHg, or use of hypertension medication. Hypercholesterolemia (HCL) was defined as low-density lipoprotein cholesterol ≥190 mg/dL or use of cholesterol-lowering medication. Anxiety and depression were evaluated by selection of one of the following statements: “I am not anxious or depressed,” “I am moderately anxious or depressed,” or “I am extremely anxious or depressed.”
Sleep scales were adapted from the US National Heart Lung and Blood Institute's Sleep Heart Health Study. Sleep duration was self-reported for workdays and nonworkdays, and a weighted average was calculated. The frequency of napping was defined as the number of times per week that the participant napped for 5 min or longer. “Frequency of disrupted breathing while sleeping” was defined as rarely if experienced <1 night per week; sometimes if experienced 1–2 nights per week; frequently if experienced 3–5 nights per week; and always or almost always if experienced 6–7 nights per week. Insomnia was assessed using a 6-item scale that measured the frequency of the following sleep symptoms: (1) trouble falling asleep; (2) waking up during the night and having difficulty getting back to sleep; (3) waking up too early in the morning and being unable to get back to sleep; (4) feeling unrested during the day regardless of the number of hours of sleep; (5) not getting enough sleep; and (6) taking sleeping pills or other medication to get to sleep. Monthly frequency of insomnia symptoms was categorized into no (one or more symptoms less than twice per month); rarely (one or more symptoms 2–4 times per month); occasionally (one or more symptoms 5–15 times per month); and frequently (one or more symptoms 16–30 times per month. Daytime sleepiness was measured using the Epworth Sleepiness Scale (ESS) and categorized as normal sleepiness (0 ≤ ESS ≤ 6); average sleepiness (7 ≤ ESS ≤); and abnormal sleepiness (9 ≤ ESS ≤ 24).
Data were presented as frequencies and percentages for categorical variables, and as mean (standard deviation [SD]) and median ( first quartile [Q1], third quartile [Q3]) for continuous variables. Differences in sociodemographics, health behaviors, health status, and sleep factors by BMI and by snoring status were assessed using Chi-square tests and analysis of variance (ANOVA) for categorical and continuous variables, respectively. Multinomial logistic regression models, with underweight/normal weight used as the reference category for BMI, were used to assess the adjusted association between BMI and snoring. Factors that were conservatively associated with both BMI and snoring using Chi-square test or ANOVA (P < 0.20) were selected a priori as covariates to be included in the adjusted models. Variables were excluded from the model if there was little variation in response (i.e., if ≥90% of the sample fell into a single response category) or if variables were highly correlated. Correlation and multicollinearity were assessed using Pearson's correlation coefficients® and variance inflation factors respectively. All analyses used a two-tailed level of significance of alpha of 0.05 and were carried out using SPSS 22 or higher (SPSS statistics IBM Corp., Armonk, NY, USA). Odds ratios and corresponding confidence intervals (CIs) and P values were generated using GENLINMIXED with the covariate (s) as fixed effect (s) and slum as a random effect.
| Results|| |
Characteristics of the sample
About 734 women were screened, and 607 were enrolled in the study. There were no significant demographic differences between women who enrolled and those who were excluded or chose not to participate at the time of recruitment. Participants were, on an average, 50 years of age and a majority reported their religion as Hindu (84.2%) [Table 1]. Nearly two of three had no formal schooling (62.8%) and were employed (61.4%). Half were married (51.4%), belonged to a scheduled caste or tribe (47.1%), and 53% lived in a household with a monthly income of 3000–10,000 Indian rupees (1 USD = 66.78 INR). One out of three women were obese (38.7%) and 36% snored. The average BMI of the women in the study was 23.7 kg/m (SD = 5.15; median = 23.0; interquartile range = 7.0).
|Table 1: Sociodemographic characteristics of the study sample of slum-dwelling women in Mysore, India (n=607)|
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Association between body mass index and snoring
The prevalence of snoring increased with higher BMI (P < 0.001) [Table 2]. Moderate and HS were three times more prevalent among women that were categorized as obese II than underweight/normal-weight women.
|Table 2: Factors associated with body mass index among slum-dwelling women in Mysore, India (n=607)|
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Factors associated with body mass index
Women who were overweight or obese were slightly younger (P = 0.065) and generally more educated (P < 0.001) than underweight/normal-weight women [Table 2]. The proportion of obese women with secondary schooling or above was three times that of underweight/normal-weight women. Current use of smokeless tobacco generally decreased with increasing BMI (P = 0.001), while the prevalence of diabetes (P < 0.001), hypertension (P < 0.001), and abnormal daytime sleepiness (P = 0.054) increased with BMI. The proportion of diabetic women classified as obese II was nearly three times that of underweight/normal-weight women. Abnormal daytime sleepiness among obese II women was four times that of underweight/normal-weight women.
Factors associated with snoring
Women who snored were older and had a higher prevalence of diabetes (P < 0.001), hypertension (P < 0.001) [Table 3]. Abnormal daytime sleepiness (P < 0.001) increased with increased frequency of snoring. The prevalence of diabetes among habitual snorers was nearly double that of nonsnorers. The prevalence of abnormal daytime sleepiness among moderate and habitual snorers was triple that of nonsnorers. The prevalence of anxiety/depression (P = 0.05) was also higher among snorers compared to nonsnorers.
|Table 3: Factors associated with snoring among slum-dwelling women in Mysore, India (n=607)|
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Selection of covariates for the multinomial logistic regression models
Three factors were associated with both BMI and snoring (P < 0.20) and were included in the regression models: age, diabetes, and hypertension.
Odds of being overweight
After adjustment for covariates, odds of being overweight did not significantly differ between moderate snorers and nonsnorers (P = 0.163) or between habitual snorers and nonsnorers (P = 0.243) [Table 4]. Age and hypertension, however, were significantly associated with BMI. The odds of being overweight decreased by 5.0% with every 1-year increase in age (P = 0.006) and was nearly four times higher among women with hypertension compared to those without hypertension (P = 0.002).
|Table 4: Odds of being overweight versus underweight/normal weight among slum-dwelling women in Mysore, India (n=607)|
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Odds of being obese I
After adjusting for covariates, odds of being obese I among moderate and habitual snorers was more than double that of nonsnorers (adjusted odds ratio: 2.42; 95% CI: 1.01–5.82, P = 0.048 and 2.10; 95% CI: 1.31–3.39, P = 0.002, respectively) [Table 5]. Age, diabetes, and hypertension were also significantly associated with BMI. The odds of being obese I decreased by 5.9% with every 1-year increase in age (P < 0.001). The odds of being obese I was nearly double among people with diabetes compared to nondiabetics (P = 0.011). The odds of being obese I was more than five times higher among women with hypertension compared to those without hypertension (P < 0.001).
|Table 5: Odds of being Obese I versus underweight/normal weight among slum-dwelling women in Mysore, India (n=607)|
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Odds of being obese II
After adjusting for all covariates, the odds of being obese II (compared to underweight/normal weight) were nearly nine times higher among moderate snorers and eight times higher among habitual snorers compared to nonsnorers (P < 0.001 and P < 0.001, respectively) [Table 6]. Age and diabetes were also significantly associated with BMI. The odds of being obese II decreased by 8.8% with every 1-year increase in age (P < 0.001) and was nearly five times higher among diabetics compared to nondiabetics (P < 0.001). Although nonsignificant, the odds of being obese II was more than six times higher among women with hypertension compared to those without (P = 0.060).
|Table 6: Odds of being Obese II versus underweight/normal weight among slum-dwelling women in Mysore, India (n=607)|
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| Discussion|| |
In this sample of slum-dwelling women, snoring was associated with obesity and cardio-metabolic disorders. In an adjusted model, habitual snorers had more than double the odds of having obesity I and seven times the odds of having obesity II compared to nonsnorers. Moderate snoring was significantly associated with obesity I and obesity II after adjusting for age, diabetes, hypertension, HCL, and daytime sleepiness. Obesity was not associated with short sleep duration, napping, daytime sleepiness, sleep apnea, insomnia, or sleep medication. HS was less common among normal/underweight women, compared to women who were overweight or obese. These findings are consistent with previous studies showing that self-reported HS in women was strongly associated with age and BMI.
Growing evidence suggests that the relationship between snoring and obesity may be bi-directional. Adiposity has been shown to enhance the risk for snoring, while snoring appears to increase risk for metabolic disorders, diabetes, poor sleep quality, and daytime sleepiness. Lauderdale et al. also demonstrated that snoring moderated the association between sleep duration and BMI. In a longitudinal model, persons who reported snoring also gained more weight. While the mechanisms underlying these associations are still little understood, some research suggests that sleep-disordered breathing may influence systemic inflammation, insulin resistance, and appetite with reciprocal and bi-directional effects.
Study results are consistent with other research showing that snoring is associated with cardio-metabolic disorders such as insulin resistance, hypertension, metabolic syndrome, and type-2 diabetes. Current evidence suggests that snoring may play a role in fragmenting sleep, an important modulator of metabolic homeostasis.
There were limitations to our study. The design was cross-sectional, and hence, we are unable to determine the temporality of the relationship between snoring and obesity. Sleep variables were self-reported and subject to recall and information bias. It is possible that some of the association between snoring and obesity reflects undiagnosed sleep apnea, which has previously been associated with obesity. Although we adjusted for potential confounders, residual confounding remains a possibility, as in all observational studies. Finally, due to low cell counts, we were unable to provide precise estimates of the effect size for some of the variables. Despite these limitations, the study also has strengths. They include recruitment of a community-based sample likely to be free of the referral biases that may occur in studies with clinic-based samples. The study was designed to minimize measurement error. Data were collected using validated and standardized instruments and questionnaires administered by well-trained staff following highly structured protocols.
This study, in a sample of slum-dwelling Indian women, found a significant relationship between snoring and obesity. In contrast to previous studies that found a consistent correlation between sleep duration and BMI, we found little association between the length of night-time sleep and BMI except among those who reported snoring. Further research is needed to understand the underlying mechanisms connecting sleep-disordered breathing and BMI.
KK, PM, and the research were supported by the Global Health Equity Scholars Fellowship from the National Institutes of Health under Award Number D43 TW010540. KK was funded by the Dissertation Year Fellowship from Florida International University. PM was also funded by the National Institutes of Health under Award Number #1R15AI128714-01. For their generous assistance on this project, the authors would like to thank PHRII team who assisted with the study, and all study participants. Special thanks to Dr. Venkat Narayan of Emory University from the CARRS study and Dr. Khurram Nasir from Yale University for providing technical support and helpful guidance.
Financial support and sponsorship
KK, PM and the research reported in this publication were supported by the National Institutes of Health/FIC, NHLBI, NINDS Award # D43 TW010540. KK was also funded by the Dissertation Year Fellowship from Florida International University. PM was funded by National Institutes of Health/NIAID #1R15AI128714-01.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]