What can anthropometric measurements tell us about obstructive sleep apnoea?

Yılmaz, Ali; Akcaalan, Melike

doi:10.5603/fm.a2016.0058

Cited by 9 publications

(11 citation statements)

References 38 publications

(44 reference statements)

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“…The higher value of the subnasale to gnathion distance recorded in control groups shows that prognathic maxillary profile is more prevalent in control subjects than OSAS. Yilmaz & Akcaalan (2017) found that the lower facial height distance was higher in the non-OSAS control group for Turkish population and their emphasized that the increase of this distance allows more oxygen transmission capacity by increasing the oral cavity. Also, the facial height distance was found higher in the control group in our study.…”

Section: Discussionmentioning

confidence: 96%

Three-Dimensional Analysis of Craniofacial Shape in Obstructive Sleep Apnea Syndrome Using Geometric Morphometrics

et al. 2019

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OZDEMIR, S. T.; ERCAN, I.; CAN, F. E.; OCAKOGLU, G.; CETINOGLU, E. D. & URSAVAS, A. Three-dimensional analysis of craniofacial shape in obstructive sleep apnea syndrome using geometric morphometrics. Int. J. Morphol., 37(1):338-343, 2019 SUMMARY: Craniofacial morphology is a risk factor for obstructive sleep apnea syndrome. The general aim of this study was to investigate the craniofacial shape differences in adults with and without obstructive sleep apnea syndrome, using geometric morphometric methods. A descriptive study conducted in 106 adult Turkish subjects, consisting of 50 patients with OSAS diagnosed by polysomnography and 56 non-OSAS controls. Three dimensional craniofacial scanning processes were performed on patient subjects on the same day as the PSG. Twelve standard craniofacial landmarks were collected from each subject's 3D craniofacial scan. Geometric morphometric analysis was used to compare the craniofacial shape differences between the OSAS and non-OSAS control groups. No statistically significant difference in terms of general shape in face shapes was found between the OSAS group and control group. However, local significance differences were found. There were significant differences between the groups in some of the interlandmark distances: 11 % of the interlandmark distances were greater in OSAS patients, and 29 % were greater in controls. Greater measured distances in OSAS are concentrated in the nasal region. In the control group, the difference is not concentrated in a specific region. Given the relationship of craniofacial structural alterations and sleep disordered breathing, we hypothesized that inter-landmark distance measurements in the craniofacial anatomy of patients might be predictive of OSA. S. T.; ERCAN, I.; CAN, F. E.; OCAKOGLU, G.; CETI˙NOGLU, E. D. & URSAVAS, A. Three-dimensional analysis of craniofacial shape in obstructive sleep apnea syndrome using geometric morphometrics. Int. J. Morphol., 37(1):338-343, 2019. OZDEMIR,

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Section: Discussionmentioning

confidence: 96%

Three-Dimensional Analysis of Craniofacial Shape in Obstructive Sleep Apnea Syndrome Using Geometric Morphometrics

et al. 2019

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“…This correlation was significant among OSA patients compared to controls. [52122] Moreover, the division based on gender by investigators showed a higher prevalence of OSA in men than women. Furthermore, there is a stronger correlation between AC/HC ratio and the severity of OSA in men than women whereas the presence of stronger correlation between BMI and the severity of OSA in women than men.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, there is a stronger correlation between AC/HC ratio and the severity of OSA in men than women whereas the presence of stronger correlation between BMI and the severity of OSA in women than men. [52325]…”

Section: Discussionmentioning

confidence: 99%

“…Then, the HC, AC, NC, BMI, height, and age were measured. [5] Furthermore, using Mallampati classification (observations of the patient's throat), patients were classified into four classes. Class I: soft palate was completely visible; Class II: uvula was visible; Class III: only base of uvula was visible; and Class IV: only hard palate was visible.…”

Section: Methodsmentioning

confidence: 99%

“…Early diagnosis as well as proper measures are essential to prevent serious complications. [5] Although more attention has been paid to OSA in recent years, this disorder could be remained unrecognized and untreated in more than 80% of cases. [67] Polysomnography (PSG) is the most definitive approach to detect OSA, but it requires expert technical service and is considered as a costly procedure and not readily available.…”

Section: Introductionmentioning

confidence: 99%

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The prediction of obstructive sleep apnea severity based on anthropometric and Mallampati indices

et al. 2019

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Background: Obstructive sleep apnea (OSA) is a common health issue with serious complications. Regarding the high cost of the polysomnography (PSG), sensitive and inexpensive screening tools are necessary. The objective of this study was to evaluate the predictive value of anthropometric and Mallampati indices for OSA severity in both genders. Materials and Methods: In a cross-sectional study, we evaluated anthropometric data and the Mallampati classification for the patients ( n = 205) with age >18 and confirmed OSA in PSG (Apnea–Hypopnea Index [AHI] >5). For predicting the severity of OSA, we applied a decision tree (C5.0) algorithm, with input and target variables considering two models (Model 1: AHI ≥15 with Mallampati >2, age >51 years, and neck circumference [NC] >36 cm and Model 2: AHI ≥30 with condition: gender = female, body mass index (BMI) >35.8, and age >44 years or gender = male, Mallampati ≥2, and abdominal circumference (AC) >112 then AHI ≥30). Results: About 54.1% of the patients were male. Mallampati, age, and NCs are important factors in predicting moderate OSA. The likelihood of moderate OSA severity based on Model 1 was 94.16%. In severe OSA, Mallampati, BMI, age, AC, and gender are more predictive. In Model 2, gender had a significant role. The likelihood of severe OSA based on Model 2 in female patients was 89.98% and in male patients was 90.32%. Comparison of the sensitivity and specificity of both models showed a higher sensitivity of Model 1 (93.5%) and a higher specificity of Model 2 (89.66%). Conclusion: For the prediction of moderate and severe OSA, anthropometric and Mallampati indices are important factors.

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Obstructive sleep apnea and metabolic disorders in morbidly obese adolescents

Haim

Daniel

Hershkovitz

et al. 2021

Pediatric Pulmonology

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Background Little known about the prevalence of obstructive sleep apnea (OSA) in morbid obese adolescents and the association between OSA and comorbid factors. Aim To examine the association between apnea–hypopnea index (AHI, a measure for OSA severity) and metabolic morbidity among morbidly obese adolescents. Methods We performed a population‐based retrospective cohort study by reviewing sleep study, metabolic indices, and comorbidity‐related data of a cohort (n = 106) of adolescents referred to a bariatric surgery clinic. We compared subjects with moderate/severe OSA (AHI ≥ 5) versus no/mild OSA (AHI < 5) OSA and three groups of subjects with increasing body mass index (BMI) concerning sleep‐study and metabolic indices using univariate analyses. To assess the link between AHI and ferritin levels a multivariate linear regression (adjusted for BMI and mean cell volume) was preformed. Results A total of 71 patients met the inclusion criteria. Subjects with moderate/severe OSA (n = 32, 45%) had higher BMI, cholesterol, cholesterol/high‐density lipoprotein (HDL) ratio, hemoglobin A1c, and serum ferritin levels (p < .05). AHI significantly increased across BMI strata (p = .02). Multivariate linear regression indicated that moderate/severe OSA was associated with higher levels of ferritin, unstandardized β = 49.1 (nIU/ml) (p = .025). Conclusions Morbidly obese adolescents with moderate/severe OSA versus no/mild OSA have a higher risk for metabolic complications. Therefore, OSA management should be considered in adolescents with morbid obesity, in addition to weight loss.

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What can anthropometric measurements tell us about obstructive sleep apnoea?

Cited by 9 publications

References 38 publications

Three-Dimensional Analysis of Craniofacial Shape in Obstructive Sleep Apnea Syndrome Using Geometric Morphometrics

Three-Dimensional Analysis of Craniofacial Shape in Obstructive Sleep Apnea Syndrome Using Geometric Morphometrics

The prediction of obstructive sleep apnea severity based on anthropometric and Mallampati indices

Obstructive sleep apnea and metabolic disorders in morbidly obese adolescents

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