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Table of Contents   
EPIDEMIOLOGICAL WORK  
Year : 2020  |  Volume : 31  |  Issue : 5  |  Page : 784-790
Epidemiological study on prevalent risk factors and craniofacial skeletal patterns in obstructive sleep apnea among South Indian population


1 Department of Orthodontics and Dentofacial Orthopedics, Amrita School of Dentistry, Amrita Vishwa Vidyapeetham, Kerala, India
2 Consultant in Public Health Research, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kerala, India

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Date of Submission06-Mar-2019
Date of Decision16-Oct-2019
Date of Acceptance29-Oct-2019
Date of Web Publication08-Jan-2021
 

   Abstract 


Context: Obstructive sleep apnea (OSA) is a condition affecting the upper airway among a vast number of people around the world. Aims: To determine the prevalent risk factors of OSA and its association with craniofacial skeletal pattern. Settings and Design: Cross-sectional, community-based study. Materials and Methods: In the first stage, questionnaire and physical examination were done for 1000 subjects between 20 and 70 years of age. Subjects were categorized as snorers and non-snorers. Snorers were further grouped as high-risk and low-risk snorers. In the second stage, polysomnography (PSG) was done for randomly selected high-risk subjects. Craniofacial skeletal pattern of OSA-diagnosed subjects were compared with non-OSA subjects using lateral cephalograms. Statistical Analysis: Analysis was performed using IBM SPSS 20. Independent sample t-test was used. A P value < 0.05 was considered as statistically significant. Results: The study population represented the following: high-risk snorers: 22.4%, low-risk snorers: 13.9%, and non-snorers: 63.7%. Excessive daytime sleepiness was present in 7.7%. Among high-risk, 80 underwent PSG, and 75 were diagnosed as OSA (94%) and 5 non-OSA subjects. Increased body mass index and neck circumference were statistically significant. Cephalometric evaluation showed difference in maxillomandibular relationship, narrowing of airway space, and inferiorly displaced hyoid. Conclusion: OSA is a major public health problem. Obesity is a strong predictor for OSA. Thus, high-risk subjects for sleep apnea could be identified using routine clinical examination, investigations, and anthropometric parameters.

Keywords: Craniofacial pattern, epidemiology, obesity, obstructive sleep apnea, upper airway

How to cite this article:
Ghosh P, Sapna Varma N K, Ajith V V, Prabha RD, Raj M. Epidemiological study on prevalent risk factors and craniofacial skeletal patterns in obstructive sleep apnea among South Indian population. Indian J Dent Res 2020;31:784-90

How to cite this URL:
Ghosh P, Sapna Varma N K, Ajith V V, Prabha RD, Raj M. Epidemiological study on prevalent risk factors and craniofacial skeletal patterns in obstructive sleep apnea among South Indian population. Indian J Dent Res [serial online] 2020 [cited 2021 Jan 28];31:784-90. Available from: https://www.ijdr.in/text.asp?2020/31/5/784/306443



   Introduction Top


Obstructive sleep apnea (OSA) is a condition resulting in increased collapsibility of the upper airway during sleep, leading to reduced or absolute cessation of airflow. Disruptive snoring, recurrent periods of complete, or limited pharyngeal obstruction causes nocturnal hypoxemia and excessive daytime sleepiness (EDS).[1] Men are more predisposed to OSA than women.[2] Obesity is the main risk factor for OSA, and different parameters such as altered body mass index (BMI), neck circumference, waist circumference, and waist-to-hip ratio are all considered as risk factors. However, this relationship appears to be varied by social, environmental, and different ethnic backgrounds. For lesser degrees of obesity, Asians are at more risk than Caucasians.[3] Craniofacial morphology is also increasingly accepted as an important interacting factor in OSA pathogenesis.[4]

Previous studies reported a prevalence rate of sleep-disturbed breathing (5%–26%) and symptomatic sleep apnea (3%-4%).[5],[6],[7],[8],[9] Undiagnosed sleep apnea represents a main public health problem. Despite adequate access to healthcare, upto 80% of cases of moderate or severe OSA have gone undiagnosed. In this study, we aim to determine the prevalence of risk factors of OSA among South Indian population and its association with craniofacial skeletal pattern.


   Materials and Methods Top


  • Community-based, two-stage, cross- sectional study.
  • Inclusion criteria: Subjects of Kochi, Kerala.
  • Age 20–70 years.
  • Exclusion criteria: recent upper airway surgery, hypothyroidism, respiratory malignancy, congestive heart failure, pregnancy, orthognathic surgery/orthodontic treatment, craniofacial syndrome.
  • The study protocol was evaluated and approved by the institutional ethical committee at Amrita Institute of Medical Sciences, Kochi.
  • Based on the information on the prevalence rate of sleep apnea among adult Indian population by Reddy et al.[1] and with 95% confidence and 20% allowable error, the minimum sample size comes to 1000 subjects.


Sampling strategy

Stratified random cluster sampling was done using two-stage strategy. The study clusters were randomly selected from the most densely populated areas in Kochi with 10 km as radius. It included 40 clusters (28 urban and 12 rural clusters), and each cluster consisted of 25 subjects. Electoral roll served as the sampling frame. From each cluster, one electoral ward was randomly selected from which subjects of either gender who satisfy the inclusion criteria were recruited for the study.

Stage 1 – Screening

A door-to-door survey was conducted and consented subjects were given the modified version of the Berlin questionnaire translated into Malayalam language, in the presence of their partner. The fidelity of the questionnaire translated in Malayalam was confirmed by back-translation into English.[1] It included queries regarding demographics, medical history, sleep symptoms, and Epworth Sleepiness Scale (ESS). The Berlin questionnaire consists of three groups of questions mainly on severity and frequency of sleep apnea, its symptoms, and risk factors associated with it. The patients were classified according to their responses as high risk or low risk: high risk if scores for two or more categories are positive and low risk if there is only one or no category of positive scores.

The ESS measures the average daytime sleepiness of a person based on the chances of dozing in eight different situations.[10] A total of 11 or more scores indicates an abnormal level of daytime sleepiness.

A limited physical examination was done to record

  • BMI: weight (kg)/height (m2).


  • According to the World Health Organization protocol,

    • Normal BMI = 18.5–24.0 kg/m2,
    • Overweight = 25-29.9 kg/m2
    • Obesity = >30 kg/m2


    • Neck circumference: measured at the level of cricothyroid membrane. In obesity, >37 cm for males and >34 cm for females.[11]
    • Waist circumference: assessed from the midpoint between the top of the iliac crest and the last palpable rib.[12]


      • Normal = men 78–94 cm, women 64–80 cm
      • Overweight = men 95–102 cm, women 80–88 cm
      • Obese = men >102 cm, women >88 cm


    • Waist-to-hip ratio: hip circumference is measured at the greater trochanter and waist circumference from the point between the top of the iliac crest and the last palpable rib.[12]
    • Obesity, W/H = >0.95 in males and > 0.88 in females.
    • Subjects presently on antihypertensive medication or if they satisfy the Joint National Committee 7 (JNC7) criteria were considered hypertensive.[13]


    Stage 2 – Polysomnography and lateral cephalogram

    Based on the questionnaire and physical examination, the subjects were classified into high-risk and low-risk groups. High-risk candidates were called for overnight polysomnography (PSG) test. After counseling, home sleep study was done for consented candidates. Home sleep test was done using Philips Respironics Alice Night machine. According to the standard criteria, the records of sleep data were scored manually by the laboratory technician who was blinded to clinical data.

    For evaluation of craniofacial skeletal pattern, lateral cephalograms were also recorded for randomly selected 40 PSG-diagnosed OSA subjects at natural head position. For comparison, lateral cephalograms of 40 subjects from the non-snoring control group were also evaluated. The variables evaluated are given in [Table 1] and [Figure 1]. The airway was analyzed as described by Battagel et al.[14]
    Table 1: Cephalometric variables evaluated

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    Figure 1: Cephalometric variables

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    Statistical analysis

    Statistical analysis was performed using IBM SPSS 20 (SPSS Inc., Chicago, IL, USA). The results are given as mean ± standard deviation for all continuous variables and as percentages for categorical variables. For the comparison between groups, Independent sample t-test was used. A P value < 0.05 was considered as statistically significant.


       Results Top


    Within a span of 18 months, 1000 subjects from Kochi population were surveyed. A flow diagram showing the number of subjects recruited at each step is shown in [Figure 2].
    Figure 2: Flowchart of the study

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    Among the 1000 respondents, men were 46.7% and women were 53.3%. The mean age and BMI of the study group were 47 ± 13 years and 25.3 ± 4.2 kg/m2, respectively. Out of the 1000 screened subjects using modified Berlin questionnaire, snoring was present among 36.3%. Non-snorers comprised 63.7%. EDS was identified in 7.7% of subjects using ESS.

    The study population also indicated that 10.4% were smokers and 15.7% were alcoholics. Descriptive statistics of Kochi population are given in [Table 2] and [Figure 3].
    Table 2: Descriptive statistics of the study population

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    Figure 3: Mean distribution of variables in the study population

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    Among the 363 snorers, based on the questionnaire, 224 high-risk and 139 low-risk snorers were identified. Comparison of high- and low-risk snorers is given in [Table 3]. By comparing the high-and low-risk subjects, BMI and neck circumference showed statistically significant results (P < 0.05).
    Table 3: Comparison between high-risk and low-risk subjects

    Click here to view


    Polysomnography

    From 224 high-risk subjects, randomly selected 80 consented subjects were tested using PSG, which forms the gold standard for diagnosing OSA. Out of 80 subjects, 53 were male and 27 were females. The mean age and BMI were 51.43 ± 12.3 years and 29.40 ± 2.79 kg/m2, respectively.

    Out of the 80 subjects who underwent PSG, 75 were diagnosed as OSA and 5 as non-OSA. Comparison of OSA and non-OSA subjects is described in [Table 3].

    Out of the 75 subjects

    • Mild OSA = 18 (AHI [Apnea Hypopnea Index] = 5-15)
    • Moderate OSA = 34 (AHI=15-30)
    • Severe OSA = 23 (AHI >30)


    Craniofacial and airway assessment

    Craniofacial assessment was done using cephalometric analysis with 40 randomly selected subjects from the 75 PSG-diagnosed OSA subjects. For comparison, 40 controls from non-snoring group were also randomly allocated. Maxillomandibular relationship (ANB), narrowing of upper, middle, and inferior airway space, distance from hyoid to mandibular plane, and length of soft palate showed statistically significant results (P < 0.05). The results are summarized in [Table 4].
    Table 4: Cephalometric comparison between OSA and control group

    Click here to view



       Discussion Top


    Population studies offer important evidence regarding the risk factors and prevalence for a particular disease condition. This is the first South Indian community-centered prevalence study to report on the prevalence of risk factors associated with OSA. Indian studies by Reddy et al.[1] and Sharma et al.,[15] Korean study by Kim et al.,[16] and Colombian study by Ruiz et al.[17] also have used two-stage probability sampling, thus reducing the chance of selection bias.

    The anthropometric measurements of obesity, such as BMI, waist and neck circumference, and waist-to-hip ratio are all regarded as strong predictors of sleep disorders. The association between various anthropometric indices and sleep apnea had been studied previously by Kang et al.[18] among Koreans, Chen et al.[19] among Chinese, Stradling et al.[20] among Europeans, and Hiestand et al.[5] among Americans. Hypertension has also been identified as an important independent risk factor of OSA by Yusoff et al.[21] among Malaysians, Tanigawa et al.,[22] and Cui et al.[23] among Japanese.

    Diagnosis of OSA was made among the high-risk snorers using PSG which is considered as the gold standard in diagnosing OSA. PSG was done at home settings for consented subjects. Kim et al. found that the mean values of AHI were not significantly different comparing laboratory PSG with that of the home PSG.[16] Thus, home sleep test was used in this study.

    In this study, the prevalence of OSA among high-risk candidates was found to be high (94%). Based on the questionnaire, 36.3% of snorers were reported and was similar to that reported by Singh et al. among Lucknow population,[24] Kim et al. in Korean population,[16] and Duran et al. in Spain population.[8] Other Indian studies by Udwadia et al.,[25] Reddy et al.,[1] Sharma et al.[26] reported snoring was 26%, 18%, and 25.6%, respectively. However, snoring symptoms among US population was as high as 52%–54%.[5],[27]

    EDS was evaluated using ESS, and EDS was present in 7.7% of the subjects which was similar to South Delhi population, 9.3%[1] and 8.8% among Chinese.[28] Among the snoring subjects, 224 were categorized as high-risk and 139 as low-risk subjects.

    Studies in the United States and Europe reported the prevalence estimate among high-risk for OSA to be 26% using Berlin questionnaire[5],[27] which is in agreement with this study which also shows a prevalence of 22.4% high risk for OSA. Studies from Asia determining prevalence are rare. Indian studies conducted using the overnight PSG reported prevalence estimate of OSA from 13.74% to 19.5%.[25]

    In India, the prevalence of OSA has been estimated only by few studies. Udwadia et al.[25] reported high prevalence rate, but it was not a community study and only men were included in the study. Reddy et al.[1] studied adult Indian population and found the prevalence of OSA to be 9.3%.

    The high-risk subjects were higher among men (64.2%) when compared with women (35.8%). These findings are similar to those by Young et al.[29] and Ancoli Isreal et al.[30] A two- to three-fold higher prevalence of OSA is reported among men[31] in all population studies. Differences in anatomy of the upper airway, its muscle function, differential distribution of adipose tissue, and effects could be the reasons for the variations among gender.

    Obesity is the main risk factor of OSA, and different obesity parameters determined among South Indian population were strongly related to OSA. The risk for OSA increases 8–12 times in persons with a BMI greater than 28 kg/m2.[32] OSA-diagnosed subjects showed an increased BMI of 29.5 kg/m2 among South Indian subjects. Studies by Smith et al. have distinctly shown that a higher BMI increases the chance for OSA, and longitudinal studies have shown improvement in AHI scores with weight loss among obese subjects, suggesting that obesity plays a contributory role.[33]

    Fat deposition around parapharyngeal region results in a reduction in caliber and a change in shape of the upper airway leading to collapsibility. A reduction in lung volumes is often associated with obesity, especially decreasing functional residual capacity, contributing to decreased tracheal tug leading to decrease in upper airway size and increased airflow resistance.[34] Another parameter of obesity such as neck circumference was 37.8 cm among high-risk which was statistically significant when compared with low-risk subjects. Svennson et al. showed that neck circumference was the most important risk factor for snoring with increasing BMI in obese than in lean women.[35] Other variables such as waist circumference and waist-to-hip ratio were not associated with high-risk subjects in South Indian population, but Suwanprathes et al. among Thailand population identified that greater waist circumference was associated with increased risk of OSA.[36]

    Numerous longitudinal, cross-sectional, and interventional studies by Kim et al.[16] and Cui et al.[23] have found an association between hypertension and OSA.[37] Though hypertension was common among South Indian subjects with OSA, it was not associated with high-risk subjects.

    For treatment planning and prediction of treatment outcome, assessing the craniofacial features of OSA subjects is important. Previous studies by Ono et al.,[38] Isono et al.[39] Friedlander et al.,[40] and Sforza et al.[41] have found that the relationship between muscle function and anatomy of the upper airway is important in understanding OSA. Any anatomic deviations in the craniofacial structures play a significant role in the pathogenesis of OSA. Abnormalities in the upper airway may compromise the pharyngeal airspace and increase the risk of OSA.

    Among OSA subjects, statistically significant narrowing of airway spaces was estimated. Since the upper airway is a soft tube without bony support, abnormalities of soft palate or the tongue volume that surround the pharyngeal airway could contribute to a decrease in the size of the pharyngeal airway as reported by Lyberg et al.[42] Increase in the length of soft palate was in agreement with Tangugsorn et al. who showed an increase in length and width of the soft palate resulted in occupying more space in pharyngeal area leading to increased airway obstruction.[43]

    The position of hyoid among patients with OSA was more displaced inferiorly compared with the control. Inferior displacement of the hyoid will pull the tongue muscles into a backward and downward position resulting in airway collapsibility among patients with OSA.[40] Whereas hyoid to third vertebrae did not show any positive results. Thus, cephalometric analysis together with various parameters of OSA is highly recommended as one of the most important tools in diagnosing and treatment planning for patients with OSA.

    The prevalence of risk factors of OSA among low-risk subjects are to be evaluated in future studies. The low-risk subjects were not included for the sleep study which could be a reason for bias since these subjects were not willing for an overnight PSG, as these diagnostic tests require high financial considerations.


       Conclusion Top


    Disturbed sleep predisposes to weakened neurocognitive function, leading to sleep-deprived accidents and poor quality of life. This study exhibited a strong association of randomly selected high-risk snorers and OSA. Various anthropometric parameters for obesity and craniomaxillofacial deformities were also reported as strong predictors of sleep apnea.

    Clinical examination and investigation of anthropological parameters demonstrated an association of high-risk subjects with OSA. The alarming rate of patients with sleep apnea identified through the study indicates the need for essential early OSA screening and awareness.

    Acknowledgement

    The authors thank the support of all staff and colleagues of the Department of Orthodontics and Department of Public Health Research, Amrita Institute of Medical Sciences.

    Financial support and sponsorship

    Nil.

    Conflicts of interest

    There are no conflicts of interest.



     
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    Correspondence Address:
    Dr. N K Sapna Varma
    Department of Orthodontics and Dentofacial Orthopedics, Amrita School of Dentistry, Ponekkara - 682 041, Kochi, Kerala
    India
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    Source of Support: None, Conflict of Interest: None


    DOI: 10.4103/ijdr.IJDR_224_19

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