Obstructive sleep apnea: a canonical correlation of cephalometric and selected demographic variables in obese and nonobese patients.

Abstract

One hundred male obstructive sleep apnea (OSA) patients were classified into 2 groups on the basis of body mass index (BMI): 43 nonobese (BMI < 30 kg/m2) and 57 obese (BMI > or = 30 kg/m2) patients. A comprehensive cephalometric analysis with a multivariate statistical method was performed in order to define the different principal components (PCs) of cervico-craniofacial skeletal and upper airway soft tissue morphology in each group and how they contributed to selected elements of the patient demographic data, ie, apnea-hypopnea index (AHI), nocturnal oxyhemoglobin saturation, and BMI. Thirty cephalometric variables of cervico-craniofacial skeletal morphology were reduced to 8 PCs describing 84.4% and 85.4% of the total variance in obese and nonobese OSA patients, respectively. Sixteen cephalometric variables of hyoid bone position and head posture were reduced to 4 PCs describing 84.4% and 85.9% of the total variance in obese and nonobese OSA patients, respectively. Twenty cephalometric variables of upper airway soft tissue morphology were reduced to 7 PCs describing 89.5% and 84.6% of the total variance in obese and nonobese OSA patients, respectively. For further analysis of PCs, a stepwise multiple regression analysis was chosen. Two dependent variables of interest are the minimal distance of the posterior pharyngeal airway space (PASmin) and AHI. PASmin accounted for 95.3% (obese OSA group) and 74.3% (nonobese OSA group) with 7 PCs and AHI for 46% with 3 PCs in both groups. Three canonical variables and their correspondents with different loadings were established differently for both OSA groups. A canonical correlation successfully clarified the complexity of simultaneous relationship of the relevant variables. These analyses are proved useful to demonstrate the relationship of cervico-craniofacial skeletal and upper airway soft tissue morphology and selected demographic data. This lays down a basis for understanding the complicated pathogenic components of obese and nonobese OSA patients.