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Although it has been speculated that rising asthma rates may be partly due to increasing obesity, the causal mechanisms that relate these conditions are unclear. We assessed the extent to which sleep-disordered breathing (SDB) may explain associations between obesity and wheezing/asthma. A total of 788 participants (aged 8-11 years) in a community-based cohort study were classified according to two outcomes: wheezing and asthma. Sleep apnea was defined as an increased number of apneas and hypopneas on overnight monitoring. SDB was identified on the basis of either sleep apnea or habitual snoring. Multiple logistic regression models showed that children with wheeze were significantly more likely to be male (odds ratio [OR] 1.62; confidence interval [CI] 1.15, 2.29), black (OR 1.90; CI 1.35, 2.29), obese (OR 1.57; CI 1.10, 2.44), and have a maternal history of asthma (OR 1.93; CI 1.16, 3.22). Further adjustment for SDB attenuated the association between obesity and wheeze (OR 1.45; CI 0.93, 2.26), but did not substantially alter the association between obesity and asthma. We conclude that SDB and obesity each are associated with asthma and wheeze. The relationship between obesity and wheeze may be partly mediated by factors associated with SDB. [ABSTRACT FROM AUTHOR]

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.