Rationale: Metabolic syndrome (MetS) affects 4 to 10% of adolescents. Risk factors include overweight, male sex, and Hispanic ethnicity. Although sleep-disordered breathing (SDB) has been implicated as a risk factor for MetS in adults, its association with SDB in adolescents is unknown. Objectives: To define the association of SDB with MetS in adolescents. Methods: Standardized measurements of SDB, anthropometry and bioassays, were made in 270 adolescents, aged 13.6 ± 0.7 years. MetS was identified if threshold levels were exceeded in three of five areas: waist circumference, blood pressure, triglyceride level, high-density lipoprotein cholesterol level, and glucose levels. Measurements and Main Results: Although 70% of children with SDB (apnea–hypopnea index ⩾ 5) were overweight and 59% had MetS, 16% of children without SDB had MetS. Twenty-five percent of those with MetS had SDB. After adjusting for age, race, sex, and preterm status, children with SDB had a 6.49 (95% confidence interval, 2.52, 16.70) increased odds of MetS compared with children without SDB. Indices of SDB stress associated with MetS included respiratory event frequency, degree of oxygen desaturation, and sleep efficiency. Analyses of individual metabolic parameters showed that, after adjustment for body mass index, SDB was associated with systolic and diastolic blood pressure, low-density lipoprotein cholesterol, and fasting insulin levels. Conclusions: A majority of adolescents with SDB are overweight and meet criteria for MetS. The close association between MetS and SDB and their putative interacting pathophysiologies suggests a need to develop screening, prevention, and treatment strategies for both disorders in high-risk, overweight adolescents.

The American Academy of Sleep Medicine Task Force on Respiratory Scoring reviewed the evidence that addresses: the validity of specific sensors in detecting airflow, tidal volume, oxyhemoglobin saturation, and CO2; the reliability of specific scoring approaches for quantifying sleep related breathing disorders (SRBD); and the validity of using various definitions of the apnea hypopnea index (AHI) as assessed by the strength and consistency of associations with several comorbidities (hypertension, cardiovascular disease, sleepiness, impaired quality of life, and accidents). The evidence was based on a literature search of relevant articles published through December 2004, which resulted in identifying and extracting data from 182 articles, which were graded using standardized approaches. Diverse physiological sensors have been utilized to quantify airflow limitation in patients with suspected SRBD. Although thermistry appears appropriate for identifying apneas, the available evidence did not indicate it provides valid quantification of airflow reduction. The emerging evidence evaluating the accuracy of signal detection against the gold standard measurements (e.g., pneumotachography) suggested the superiority of inductance plethysmography and nasal pressure transducers for detection of hypopneas, with some evidence that recordings from a nasal pressure transducer may better approximate flow/volume than uncalibrated inductance plethysmography. However, since the nasal pressure transducer has only recently been incorporated into large-scale studies, there are as of yet few data that address the predictive value of transducer-identified events relative to clinical or physiological outcomes. Very few studies directly compared the validity of alternative approaches for defining the duration, amplitude change, and use of corroborative data from desaturation or arousal for defining hypopneas. Many observational studies utilizing various designs and approaches for event detection have shown significant associations between measures of SRBD and health outcomes. Data from the 2 largest sleep cohort studies, the Sleep Heart Health Study and the Wisconsin Sleep Cohort, both used definitions of hypopneas based on "discernible" reductions of inductance plethysmography signals with associated desaturation and showed that the derived AHIs using these hypopnea definitions correlated with various indices of morbidity. However, it is not clear whether alternative definitions would provide comparable if not better prediction, or whether optimal approaches for event identification would vary for different outcomes. Despite these limitations, forming a consensus on optimal approaches for recording and measuring respiratory events is an important step toward generating data from different clinical or research laboratories that can be compared. However, additional research is needed, including direct comparisons of alternative measuring approaches for predicting clinical outcomes, with a need to address these issues in large samples across the age spectrum and with inclusion of promising new technology.