International Journal of Pediatric Otorhinolaryngology 78 (2014) 854–859
Contents lists available at ScienceDirect
International Journal of Pediatric Otorhinolaryngology journal homepage: www.elsevier.com/locate/ijporl
Metabolic alterations in children with obstructive sleep apnea§ Bharat Bhushan a,b,*, John Maddalozzo a,b, Stephen H. Sheldon c,d, Shannon Haymond e,f, Karen Rychlik g, George C. Lales h, Kathleen R. Billings a,b a
Division of Otolaryngology – Head and Neck Surgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States Northwestern University Feinberg, School of Medicine, Chicago, IL, United States c Division of Pulmonology, Ann and Robert H. Lurie Children’s Hospital of Chicago Sleep Medicine Center, Chicago, IL, United States d Department of Pediatrics, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States e Department of Pathology and Laboratory Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States f Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States g Biostatistics Research Core, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Chicago, IL, United States h Clinical and Translational Research Program, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Chicago, IL, United States b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 30 December 2013 Received in revised form 24 February 2014 Accepted 26 February 2014 Available online 7 March 2014
Importance: The incidence of obesity is rising in the United States and has been linked to Obstructive Sleep Apnea (OSA) even in young children. Understanding the role that obesity and OSA play in alterations in metabolic variables that can lead to serious health issues is essential to the care and counseling of affected children. Objectives: To evaluate the association of alterations in metabolic variables, including insulin resistance, to OSA in young, obese children. Design: Retrospective, case-control series. Setting: Tertiary care children’s hospital. Participants: Obese children aged 2-12 years who had undergone overnight polysomography and routine laboratory testing for lipid levels, fasting glucose, and insulin from January 1, 2006 to December 31, 2012 were identified from a TransMed Bio-Integration Suite and Epic’s clarity database search. Results: A total of 76 patients were included for analysis. Forty-three (56.6%) were male, and the mean age was 8.3 2.5 years (range, 2.4–11.9 years). The mean body mass index (BMI) z score was 2.8 0.75 (range, 1.7–6.3), and all patients were obese (BMI z score > 95th percentile). Twenty two patients (28.9%) had an apnea–hypopnea index (AHI) 2.67 for boy and >2.22 for girls) [20]. Height and weight data were collected on each patient. The BMI was calculated (weight/height2), and the BMI z-score was computed using Center for Disease Control (CDC) growth standards (www.cdc.gov/growthcharts) and online software (www.cdc.gov/epiinfo). Patients with a BMI z score above the 95th percentile were considered obese. Blood pressure (BP) readings were obtained either on the night of the PSG or from an outpatient visit within 3 months of the PSG. 2.3. Polysomnography A standard overnight PSG (Cadwell easy 3 version 3.9.34, Kennewick, WA, USA) was performed. The apneas and hypopneas were identified and scored according to accepted American Academy of Sleep Medicine (AASM) pediatric criteria as defined in the AASM Manual for Scoring of Sleep and Associated Events, 2007 [21]. Analysis was conducted in 30-second epochs. All 30second raw-data epochs of the recording were analyzed. The PSG was interpreted by a pediatric sleep medicine specialist. The apnea–hypopnea index (AHI) was defined as the total number of obstructive apneas and hypopneas per hour of sleep, which indicates the frequency of obstructive and partially obstructive events. Severity of OSA was classified as mild (AHI between 1 and 4.99 events per hour of sleep), moderate (AHI between 5 and 9.99 events per hour of sleep), severe (AHI 10 events per hour of sleep), and no OSA (AHI < 1 event per hour of sleep). 2.4. Statistical analysis Descriptive statistics were summarized using frequencies and percentages for categorical data, and mean and standard deviations for continuous data. To determine differences between groups of no OSA, mild OSA, moderate OSA and severe OSA, both parametric and nonparametric tests were used. Chi-square test of association was used for nominal data. T-test and analysis of variance statistics were used for normally distributed data. Mann Whitney U and Kruskal Wallis statistics were used for nonnormally distributed data. Outliers were identified and checked for accuracy. Scatter plots were used to graphically show correlations among PSG and metabolic variables. Multiple linear regression models were run to predict dependent variables for log transformed fasting insulin and logtransformed HOMA. Predictors included age, gender, TST, BMI z score, and AHI. Multinomial logistic regression models were run
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B. Bhushan et al. / International Journal of Pediatric Otorhinolaryngology 78 (2014) 854–859
to predict patients grouped into those with no OSA, mild OSA, moderate OSA, and severe OSA. Results were considered significant with two-tailed test and p < 0.05. Statistical analysis was conducted using Statistical product and service solution (SPSS) software version 20 (IBM SPSS Inc., Chicago, IL). 3. Results A total of 76 patients were studied. Forty-three (56.6%) were male, and the mean age of patients was 8.1 2.5 years (range, 2.4– 11.9 years). The mean BMI z score was 2.8 0.75 (range, 1.7–6.3), and all patients were obese (BMIz score > 95th percentile). Twenty-one patients (28.95%) had an AHI < 1/h (no OSA), 27 (35.53%) had an AHI 1 4.99/h (mild OSA), 12 (15.8%) had AHI 5
Contents lists available at ScienceDirect
International Journal of Pediatric Otorhinolaryngology journal homepage: www.elsevier.com/locate/ijporl
Metabolic alterations in children with obstructive sleep apnea§ Bharat Bhushan a,b,*, John Maddalozzo a,b, Stephen H. Sheldon c,d, Shannon Haymond e,f, Karen Rychlik g, George C. Lales h, Kathleen R. Billings a,b a
Division of Otolaryngology – Head and Neck Surgery, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States Northwestern University Feinberg, School of Medicine, Chicago, IL, United States c Division of Pulmonology, Ann and Robert H. Lurie Children’s Hospital of Chicago Sleep Medicine Center, Chicago, IL, United States d Department of Pediatrics, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States e Department of Pathology and Laboratory Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, United States f Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States g Biostatistics Research Core, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Chicago, IL, United States h Clinical and Translational Research Program, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Chicago, IL, United States b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 30 December 2013 Received in revised form 24 February 2014 Accepted 26 February 2014 Available online 7 March 2014
Importance: The incidence of obesity is rising in the United States and has been linked to Obstructive Sleep Apnea (OSA) even in young children. Understanding the role that obesity and OSA play in alterations in metabolic variables that can lead to serious health issues is essential to the care and counseling of affected children. Objectives: To evaluate the association of alterations in metabolic variables, including insulin resistance, to OSA in young, obese children. Design: Retrospective, case-control series. Setting: Tertiary care children’s hospital. Participants: Obese children aged 2-12 years who had undergone overnight polysomography and routine laboratory testing for lipid levels, fasting glucose, and insulin from January 1, 2006 to December 31, 2012 were identified from a TransMed Bio-Integration Suite and Epic’s clarity database search. Results: A total of 76 patients were included for analysis. Forty-three (56.6%) were male, and the mean age was 8.3 2.5 years (range, 2.4–11.9 years). The mean body mass index (BMI) z score was 2.8 0.75 (range, 1.7–6.3), and all patients were obese (BMI z score > 95th percentile). Twenty two patients (28.9%) had an apnea–hypopnea index (AHI) 2.67 for boy and >2.22 for girls) [20]. Height and weight data were collected on each patient. The BMI was calculated (weight/height2), and the BMI z-score was computed using Center for Disease Control (CDC) growth standards (www.cdc.gov/growthcharts) and online software (www.cdc.gov/epiinfo). Patients with a BMI z score above the 95th percentile were considered obese. Blood pressure (BP) readings were obtained either on the night of the PSG or from an outpatient visit within 3 months of the PSG. 2.3. Polysomnography A standard overnight PSG (Cadwell easy 3 version 3.9.34, Kennewick, WA, USA) was performed. The apneas and hypopneas were identified and scored according to accepted American Academy of Sleep Medicine (AASM) pediatric criteria as defined in the AASM Manual for Scoring of Sleep and Associated Events, 2007 [21]. Analysis was conducted in 30-second epochs. All 30second raw-data epochs of the recording were analyzed. The PSG was interpreted by a pediatric sleep medicine specialist. The apnea–hypopnea index (AHI) was defined as the total number of obstructive apneas and hypopneas per hour of sleep, which indicates the frequency of obstructive and partially obstructive events. Severity of OSA was classified as mild (AHI between 1 and 4.99 events per hour of sleep), moderate (AHI between 5 and 9.99 events per hour of sleep), severe (AHI 10 events per hour of sleep), and no OSA (AHI < 1 event per hour of sleep). 2.4. Statistical analysis Descriptive statistics were summarized using frequencies and percentages for categorical data, and mean and standard deviations for continuous data. To determine differences between groups of no OSA, mild OSA, moderate OSA and severe OSA, both parametric and nonparametric tests were used. Chi-square test of association was used for nominal data. T-test and analysis of variance statistics were used for normally distributed data. Mann Whitney U and Kruskal Wallis statistics were used for nonnormally distributed data. Outliers were identified and checked for accuracy. Scatter plots were used to graphically show correlations among PSG and metabolic variables. Multiple linear regression models were run to predict dependent variables for log transformed fasting insulin and logtransformed HOMA. Predictors included age, gender, TST, BMI z score, and AHI. Multinomial logistic regression models were run
856
B. Bhushan et al. / International Journal of Pediatric Otorhinolaryngology 78 (2014) 854–859
to predict patients grouped into those with no OSA, mild OSA, moderate OSA, and severe OSA. Results were considered significant with two-tailed test and p < 0.05. Statistical analysis was conducted using Statistical product and service solution (SPSS) software version 20 (IBM SPSS Inc., Chicago, IL). 3. Results A total of 76 patients were studied. Forty-three (56.6%) were male, and the mean age of patients was 8.1 2.5 years (range, 2.4– 11.9 years). The mean BMI z score was 2.8 0.75 (range, 1.7–6.3), and all patients were obese (BMIz score > 95th percentile). Twenty-one patients (28.95%) had an AHI < 1/h (no OSA), 27 (35.53%) had an AHI 1 4.99/h (mild OSA), 12 (15.8%) had AHI 5
