Because there is a lack of agreed upon diagnostic criteria, it is critical to understand the natural history of obstructive sleep apnoea (OSA) in children in order to establish treatment strategies based on objective data.The Penn State Child Cohort is a representative, general-population sample of 700 elementary school children at baseline, of whom 421 were reassessed 8 years later, during adolescence.The remission of childhood apnoea-hypopnoea index (AHI) ≥2 events per h in adolescence was 52.9%. Using the higher threshold of AHI ≥5 events per h, remission was 100.0%, with 50.0% partially remitting to AHI 2- <5 events per h and the other half remitting to AHI <2 events per h. The incidence of adolescent AHI ≥2 events per h in those with childhood AHI <2 events per h was 36.5%, while the incidence of AHI ≥5 events per h in those with childhood AHI <5 events per h was 10.6%. This longitudinal study confirms that prepubertal OSA tends to resolve naturally during the transition to adolescence, and that primary snoring and mild sleep disordered breathing (SDB) do not appear to be strongly associated with progression to more severe SDB.The key risk factors for SDB in adolescence are similar to those found in middle-aged adults (i.e. male sex, older age and obesity). Moreover, consistent with recent studies in adults, this study includes the novel cross-sectional finding that visceral fat is associated with SDB as early as adolescence.

Objective To examine the cross-sectional association between measurements of obesity and subclinical impairment of cardiac autonomic modulation (CAM) in a population-based sample of children. Methods Data from 616 grade K-5 children randomly selected from Central Pennsylvania were utilized. Obesity was defined using the International Obesity Task Force (IOTF) age and sex specific cut off criteria and classified as normal weight, overweight, and obese. CAM was measured by heart rate variability (HRV) analysis of beat-to-beat RR intervals, including time domain measures i.e., the standard deviation of all RR intervals (SDNN), the square root of the mean of the sum of squares of differences between adjacent RR intervals (RMSSD), and mean heart rate (HR); and frequency domain measures i.e., high frequency power (HF), low frequency power (LF), and LF/HF ratio. Results The prevalence of obesity and overweight in children was 12.3%, and 16.5%, respectively. Age, race, sex, and sleep disorder breathing (SDB) adjusted means (SE) of SDNN were 98(1.24), 90.2(2.58), and 81.9(3.03) milliseconds (ms) in normal weight, overweight, and obese groups, respectively; and that for (log) HF were 6.83(0.04), 6.56(0.08), and 6.35(0.09) ms2, respectively. Comparing the magnitude of effects from BMI, weight, and height percentiles, and waist circumference on HRV indices revealed that body weight was the strongest correlate of HRV indices. Conclusion Childhood obesity is significantly associated with lower HRV, indicative of sympathetic overflow unopposed by parasympathetic modulation. These findings support the need to target childhood-obesity, before traditional “high risk age” for cardiac events.

INTRODUCTION To investigate the association between abdominal obesity and metabolic syndrome (MetS) burden in a population-based sample of adolescents. METHODS We used the data from 421 adolescents who completed the follow-up examination in the Penn State Children Cohort study. Dual-energy x-ray absorptiometry (DXA) was used to assess abdominal obesity, as measured by android/gynoid fat ratio (A/G ratio), android/whole body fat proportion (A/W proportion), visceral (VAT) and subcutaneous fat (SAT) areas. Continuous metabolic syndrome score (cMetS), calculated as the sum of the age and sex-adjusted standardized residual (Z-score) of five established MetS components, was used to assess the MetS burden. Linear regression models were used to analyze the impact of DXA measures on cMetS and individual cMetS components. All models were adjusted for age, race, sex, and general obesity. RESULTS Abdominal obesity is significantly associated with increased cMetS. With 1 standard deviation (SD) increase in A/G ratio, A/W proportion, VAT area, and SAT area, cMetS increased by 1.34 (SE=0.17), 1.25 (SE=0.19), 1.67 (SE=0.17), and 1.84 (SE=0.20) units, respectively. At individual component level, strongest association was observed between abdominal obesity and insulin resistance than lipid-based or blood pressure-based components. VAT and SAT had a stronger impact on insulin resistance than android ratio-based DXA measurements. CONCLUSIONS Abdominal obesity is associated with higher MetS burden in adolescent population. The association between abdominal obesity and insulin resistance measure is the strongest, suggesting the key impact of abdominal obesity on insulin resistance in adolescents Mets burden.