Sleep-Disordered Breathing in Overweight and Obese People with Canadian Health&Care Mall

Patient Characteristics

We recruited children and adolescents who were 6 to 17 years of age who presented as overweight or obese between January 2001 and June 2006 at the Pediatric Obesity Clinic of the Antwerp University Hospital. Children were not included when they had any chronic medical condition, or any genetic, neuromuscular or craniofacial syndromes. Patients were classified as prepubertal or pubertal. All subjects underwent all measurements as part of their routine clinical evaluation. This case study was approved by the Ethics Committee of the Antwerp University Hospital.

Anthropometry

Height, weight, waist circumference, and waist/hip ratio were measured by standardized techniques. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters and was further analyzed as z-score. Children were classified as overweight or obese according to the definitions of the International Obesity Task Force. Canadian Health&Care Mall on http://healthcaremall4you.com/interesting-facts-about-diet-together-with-canadian-healthcare-mall.html gives an explanation how to reduce weight and know more about diet and its peculiar features.

UA Measurements and Analysis

Fasting blood samples were obtained from the subjects on the morning of the day of hospital admission with the use of an indwelling venous line for measurements of UA and creatinine levels. Urine collection was performed throughout a 24-h period, starting on the morning of the day of hospital admission. The calculation of UA excretion per deciliter of creatinine clearance has been proposed as the preferred means of determining UA excretion both in children and in adults, and is calculated as the product of urinary UA and serum creatinine concentrations divided by urine creatinine concentration (all in milligrams per deciliter). UA excretion of < 0.56 mg/dL was considered to be normal. The urinary UA/creatinine ratio was also calculated.

Polysomnography

Anthropometry A detailed description of the standard polysomnography procedure was described previously. Obstructive apnea was defined as the presence of chest and/or abdominal wall motion associated with a cessation of airflow. Central apnea was defined as the absence of chest and/or abdominal wall motion associated with a cessation of airflow, lasting > 10 s or of any length but associated with > 4% desaturation. Hypopnea was defined as a > 50% decrease in the amplitude of the airflow signal followed by > 4% desaturation. Respiratory disturbance index (RDI) was defined as the total number of apneas and hypopneas, as defined above, per hour of sleep. All desaturations defined as decreases of > 4% from baseline arterial oxygen saturation (Sao2) were quantified (oxygen desaturation index). Measurements associated with poor pulse tracings or following movement, were excluded. For each child, the mean Sao2, the Sao2 nadir, and the total duration of desaturation, expressed as a percentage of total sleep time, were recorded.

Statistical Analysis

The statistical analysis was performed with a statistical software package (Statistica, version 7.0; StatSoft; Tulsa, OK). All data are summarized as the mean ± SD. The Kolmogorov-Smirnov test was used to test normality. To illustrate the various degrees of SDB severity in our cohort, we compared polysomnographic characteristics among three groups, based on more recent published normative data in children, as follows: RDI 2 and 5. Comparisons among these three groups were performed with one-way analysis of variance or with the Jonckheere-Terpstra test as a nonparametric alternative.

To examine whether SDB was associated with increased UA excretion, we used the following outcome variables: serum UA; UA excretion; and urinary UA/creatinine ratio. First, the Pearson correlation coefficient was calculated between the latter outcome variables and the variables reflecting SDB severity (ie, RDI, oxygen desaturation index, mean Sao2, Sao2 nadir, or the percentage of total sleep time spent with Sao2 < 89%). In case of a significant correlation, we proceeded with multiple linear regression analysis to determine whether the correlation persisted after controlling for sex, puberty, and adiposity. Controlling for adiposity was done by the inclusion of one measure of adiposity with the highest univariate correlation coefficient with the respective outcome (ie, BMI z-score, waist circumference, or waist/hip ratio). Sex and puberty were included as dummy variables (1 for female patients; 1 for pubertal). In the case of multiple significant univariate correlations between SDB and UA variables, the SDB variables were introduced separately into the model, because of possible multicollinearity. Residual analysis was performed for each regression model to test the validity of model assumptions. For all analyses, p < 0.05 was considered to be statistically significant.

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